Pith. sign in

REVIEW 3 major objections 4 minor 182 references

CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A chest X-ray VLM co-trains classification and grounding heads with report generation, aligns with clinical rewards, and delegates measurements to tools—claiming top results and 43.6-point F1 gains.

desk verdict Solid, ambitious engineering; the CRIMSON result does not actually support the DAPO-specific claim, but the core system is real and worth refereeing. read the letter →

arxiv 2608.03890 v1 pith:RKNGIOBP submitted 2026-08-04 cs.CV cs.AI

Mercy Prasanna Ranjit , Anirban Porya , Sathvik Joel , Niharika Vadlamudi , Nikhilesh Chowdary Eathamukkala , Prasanth V V , Abhyuday Kumara Swamy , Pranay Narhari Umredkar
show 3 more authors
Pradeep Narayan Vivek Rajagopal Tanuja Ganu
This is my paper
classification cs.CVcs.AI
keywords chestX-rayvision-languagemodelauxiliarysupervisionreinforcementlearningspatialgroundingreportgenerationtool-augmentedmeasurementclinicalevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a chest X-ray vision-language model—a model that reads an image and produces text—can be made clinically useful by combining three things usually kept separate: thresholdable diagnostic classification, spatial grounding, and free-text report generation. CARE-X attaches focal-loss classification heads and a composite-loss bounding-box head to an autoregressive generative backbone and co-trains them with the language-modeling objective; the authors report that this co-training improves the generative outputs themselves, not just the auxiliary predictions. They then apply DAPO, a reinforcement-learning method with task-specific clinical rewards, and report gains across report generation, closed visual question answering, and spatial grounding, including on a held-out clinical metric that was never used as a reward. For diagnoses that depend on measurements, they show that letting a tool-calling VLM perceive the radiograph while deterministic tools compute ratios and thresholds yields large F1 improvements over perception-only inference. If these claims hold, a single radiology VLM could support both flexible reporting and structured, thresholdable, measurement-grounded decision support.

What carries the argument

Two mechanisms carry the argument. The first is auxiliary-head co-training: focal-loss classifiers and a bounding-box head attached to the same hidden representation that feeds autoregressive text generation, trained jointly with the language-modeling loss, so discriminative gradients flow into the shared representation. The isolation experiment compares an identical generative-only model against the co-trained model, both read out in generative mode, to attribute gains to representation enrichment. The second is DAPO (Decoupled Clip and Dynamic sAmpling Policy Optimization), an RL algorithm with asymmetric clipping and token-level loss normalization chosen to prevent entropy collapse and to

What would settle it

Train the same CARE-X configuration with the auxiliary heads' gradients blocked from reaching the shared image–text backbone (a stop-gradient ablation), keeping data and loss weights identical; if generative report and grounding metrics still improve by the same margin, the claimed representation enrichment is not the cause of the gains.

Watch

Extended reading notes

Core claim

CARE-X's central claim is that discriminative and generative objectives reinforce one another rather than compete. The model attaches two focal-loss binary classifiers (abnormality presence; abnormal tube/line placement) and a bounding-box detection head to the shared hidden layer of a 3.8B autoregressive language backbone, and co-trains these heads with the causal language-modeling loss. In an ablation where both model variants are evaluated only through generated text, co-training improves classification F1 and every one of eight grounding metrics, which the paper reads as evidence that structured supervision enriches the shared representation. On top of this, DAPO—a reinforcement-learning

Load-bearing premise

The load-bearing premise is that co-training the auxiliary heads improves the shared representation itself; if the observed gains come instead from the particular loss-weight balance or from the extra supervision acting as a generic regularizer, the paper's generalizable claim that discriminative supervision and generation reinforce one another weakens, even though the concrete model may still perform well.

Editorial extensions

If this is right

  • A single model can serve both deployment modes: free-text reports from generative decoding and structured probabilities or boxes from auxiliary heads, with classification thresholds tunable between high-sensitivity screening and high-specificity confirmation.
  • At deployment, clinicians may not need a separate detection head: DAPO-trained autoregressive grounding is close to or better than the SFT auxiliary head on some benchmarks.
  • Report-quality gains are not purely reward overfitting: CRIMSON, a severity-weighted clinical metric never used as a reward, improves alongside the rewarded metrics on all four report benchmarks.
  • Measurement-dependent diagnoses (cardiomegaly, mediastinal widening, aortic enlargement) are better computed than perceived, so hybrid VLM-plus-tools inference is a practical route to threshold-based screening.
  • The co-training-plus-reward-alignment recipe is put forward as a transferable design principle for other medical domains needing both thresholdable predictions and flexible text generation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next test, beyond this paper, is to fuse the two pipelines: use CARE-X's own grounding head to pick the landmarks that the deterministic measurement tools then measure, rather than a separate general-purpose tool-calling VLM; this could address the residual errors near decision thresholds.
  • The division-of-labour principle should transfer to other imaging tasks where diagnosis is threshold-based and deterministic measurement tools exist—for example echocardiographic dimensions, fetal ultrasound biometry, or retinal vessel calibre.
  • If the mutual-reinforcement claim is a general property, its benefit should be largest when radiology text is scarce but labels are available; the paper's rare-ICU cohort results are consistent with this but were not designed to isolate the mechanism.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper introduces CARE-X, a chest X-ray VLM that co-trains focal-loss classification heads and composite-loss grounding heads with the generative language-modeling objective, then applies DAPO reinforcement learning with task-specific rewards for report generation, closed VQA, and spatial grounding. A second, separate pipeline augments Qwen3-VL-4B-Instruct with deterministic measurement tools for ratio-based diagnoses such as cardiomegaly and mediastinal widening. The paper reports state-of-the-art results on the majority of metrics across four report-generation benchmarks, 94.0% accuracy on ReXVQA, generative spatial decoding approaching or surpassing the auxiliary detection head, and +43.6 pp average F1 for tool-augmented measurement over perception-only inference. It also presents clinical validation on a Narayana Health ICU cohort for rare conditions and on a CT-confirmed outpatient cohort for enlargement conditions.

Significance. If the results hold, the paper makes a useful contribution by integrating three otherwise separate lines of work: auxiliary discriminative heads, reward-aligned RL, and tool-augmented quantitative reasoning. The controlled comparisons are a genuine strength: Appendix B isolates co-training from the presence of auxiliary readouts, the main text compares SFT vs SFT+DAPO, and Appendix F.1 quantifies threshold-selection optimism with cross-validation. The NH clinical evaluations, especially the CT-confirmed enlargement cohort, are valuable and rare. However, two load-bearing pieces of evidence are weaker than the text claims: the CRIMSON result lacks a pre-RL comparator, and the public quantitative-reasoning evaluation shares its label-generation and tool-measurement machinery. Both are fixable, but the stated claims in Sections 5.1.1 and 5.3 currently go beyond what the experiments establish.

major comments (3)
  1. [§5.1.1, Table 2] The claim that CRIMSON "independently confirms that DAPO improvements reflect genuine clinical quality" is not supported by the reported evidence. CRIMSON is listed only for CARE-X-RL; CARE-X-SFT is a dash because the metric became available after the SFT checkpoint was submitted. Since the DAPO report-generation rewards are exactly BERTScore, RadGraph, and GREEN (Table 1, Appendix I.2), every reported SFT-to-RL gain on those metrics is at least partly a reward-optimization artifact. CRIMSON was the only independent check, but without a CRIMSON score for CARE-X-SFT it cannot attribute the CRIMSON result to DAPO rather than to the SFT/auxiliary-co-training foundation. Please compute CRIMSON for the SFT checkpoint, or revise the claim to state that CARE-X-RL achieves strong CRIMSON relative to external baselines, without attributing the gain to DAPO.
  2. [§3.2.1, §K.2, Table 6] The public quantitative-reasoning evaluation is partially circular. Appendix K.2 states that ground-truth labels for the five conditions are derived by applying the CheXStruct thresholds to CheXStruct-computed measurements, and the tool-augmented pipeline loads CXAS segmentation masks and applies the same thresholds. Thus the large F1 gains in Table 6 may reflect alignment between the label-construction machinery and the tool-pipeline machinery rather than clinically validated measurement. The CT-confirmed NH Study 2 is a better external check, but it reports only recall, not F1 or specificity. Please either report full sensitivity/specificity/F1 on an externally labeled cohort, validate the segmentation on an independent set, or explicitly frame Table 6 as demonstrating tool-orchestration capability rather than clinical measurement validity.
  3. [§3.1.2, Appendix B] The paper claims that auxiliary supervision "enriches the shared representation" and that structured prediction and generation "reinforce one another." The Appendix B ablation does show large generative-mode improvements from co-training, which is meaningful. However, the mechanism is not established: the gains could come from the auxiliary losses acting as a regularizer, from improved early-training gradient dynamics, or from loss-weight balancing, rather than from a representational enrichment that transfers to generation. Since this is stated as one of the paper's three generalizable insights, please either soften the mechanistic language or provide representation-level evidence (e.g., probing, feature-similarity analysis, or a loss-weight sensitivity study).
minor comments (4)
  1. [§3.3.1, Table 15] The text says the evaluation cohort is "annotated for five binary conditions," but Table 15 lists six conditions including Tubes & Lines Presence and Tubes & Lines Abnormal Placement. Clarify the counting (e.g., five target pathologies plus the prerequisite presence task).
  2. [§5.4, Table 8] The NH Study 2 comparison reports recall only, because all cases are CT-confirmed positives. The sentence "corroborating our central findings (Table 27)" should be qualified: recall improvement is not the same as the F1 improvement reported on the public benchmarks, and no specificity can be estimated in this cohort.
  3. [§5.1.1] The sentence beginning "Because CRIMSON is entirely independent of our training rewards" should be moved closer to the table footnote and rephrased, since the independence holds for the metric itself but not for the DAPO attribution.
  4. [Table 6, DA row] The descending-aorta row has only 7 test samples and the footnote already cautions interpretation; please also carry this caveat into the main-text summary sentence that reports the +43.6 pp average.

Circularity Check

2 steps flagged · score 6.0 of 10

Tool-augmented measurement evaluation is partially self-definitional (ground truth generated by the same tools being evaluated), and the report-generation RL gains are measured on the exact rewards DAPO optimizes, with the only held-out check lacking a pre-RL baseline.

  1. self definitional [Section 3.2.1 and Appendix K.1, Table 24; results in Table 6]
    "Ground-truth labels for each sample are derived by applying these thresholds to the CheXStruct-computed measurements. (Appendix K.1) ... classifying as abnormal when CTR≥0.50 (PA) or≥0.55 (AP). (Section 3.2.1)"

    The quantitative-reasoning evaluation defines the ground-truth label by running CheXStruct's deterministic measurement tools and thresholds. The tool-augmented pipeline under test invokes those same CheXStruct tools (measure cardiac width, measure thoracic width, compute ctr, etc.) and applies the same thresholds. A correct execution of the tool chain therefore trivially reproduces the label. The +43.6 pp F1 gain over perception-only is thus partly a tautology: it measures how faithfully the VLM calls the oracle tool chain, not whether the measurements are clinically valid against an independent standard. The NH Study-2 CT-confirmed cohort is separate, but the headline Table 6 result rests on tool-derived labels.

  2. fitted input called prediction [Section 3.1.2 (DAPO Rewards) and Section 5.1.1; Table 2]
    "Report Generation: We use BERTScore (Zhang et al., 2020), RadGraph (Delbrouck et al., 2022), and GREEN (Ostmeier et al., 2024) as the rewards."

    The report-generation metrics on which CARE-X-RL is claimed to improve and reach SOTA — BERTScore, RadGraph, GREEN — are exactly the three reward signals used in the DAPO curriculum (Table 1, Appendix I.2). The SFT→RL gains on those columns are therefore the direct result of optimizing those same metrics, not independent evidence of clinical-quality improvement. The paper attempts to break this with CRIMSON, a metric never used as a reward, but CRIMSON is reported only for CARE-X-RL and not for CARE-X-SFT, so it cannot isolate the DAPO increment; the claim that CRIMSON 'independently confirms that DAPO improvements reflect genuine clinical quality' is missing the pre-RL comparator needed to support it.

full rationale

The core CARE-X-RL contributions are not fundamentally circular: the auxiliary-head co-training ablation in Appendix B compares a generative-only backbone with a co-trained backbone under identical generative decoding, which is a genuine controlled experiment; DAPO is an externally published method; and the SOTA comparisons against MedGemma, MedVersa, and CheXOne on held-out test sets use external baselines. There is no load-bearing self-citation or imported uniqueness theorem. However, two significant reductions weaken the paper's strongest claims. First, the tool-augmented measurement experiments define ground truth with the same CheXStruct measurement tools and thresholds that the tool-calling pipeline invokes, making the headline +43.6 pp F1 improvement partly self-confirming by construction. Second, the report-generation RL results are reported on the exact BERTScore/RadGraph/GREEN rewards that DAPO optimizes, so those gains are not independent evidence of clinical fidelity; the only held-out metric, CRIMSON, was not scored for CARE-X-SFT, leaving no baseline against which the DAPO-specific improvement can be verified. These are partial circularities, not a complete collapse of the derivation, so the score is 6 rather than higher.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

CARE-X introduces no new physical or computational entities, but it does introduce several hand-set quantities: per-condition decision thresholds, spatial loss weights, and a reward curriculum. The threshold tuning on the evaluation cohort is the most notable free-parameter issue; the paper partially mitigates it with 3-fold CV. The heaviest unvalidated assumption is the CXAS segmentation quality on the NH cohort, which the authors themselves flag.

free parameters (4)
  • Per-condition decision thresholds for NH ICU conditions = Fracture 0.50, Med. Shift 0.55, Pneumoperitoneum 0.65, Pneumothorax 0.65, Tubes & Lines 0.40
    Tuned on the same 1,047 radiographs used for evaluation (Section 3.3.1, Table 7). The paper quantifies selection optimism via 3-fold CV in Appendix F.1, showing small differences, so this is a free parameter with a partially addressed caveat.
  • Composite spatial loss weights (GIoU 5.0, mIoU 0.8, L1 7.0, focal 2.0) = Weights reported in Appendix C
    The grounding head loss weighting is chosen by hand, not derived from first principles, and the grounding results depend on it.
  • Reward curriculum schedule and weights = Stage 1: BERTScore 0.5, RadGraph 0.5; Stage 2: 0.25/0.25/0.5 GREEN; Stage 3: 0.5/0.5
    The three-stage reward curriculum and its stage boundaries/weights are design choices selected to avoid reward hacking; the reported results depend on them.
  • AP/PA view-dependent thresholds = CM: CTR 0.50 (PA), 0.55 (AP); MW: MCR 0.25 (PA), 0.33 (AP)
    These are clinical thresholds from the literature used by the CheXStruct/measurement tools, not fit to data. They are included as parameters the central measurement claim depends on, but they are externally sourced.
assumptions (5)
  • domain assumption Focal loss and composite spatial loss provide useful gradient signal for the generative decoder when trained jointly.
    The co-training benefit (Appendix B) is an empirical result, not a derived property. It is also the paper's generalizable insight #1.
  • domain assumption The reward models (BERTScore, RadGraph, GREEN, CRIMSON) correlate with clinical quality sufficiently to serve as optimization targets and validation.
    The RL claims rest on reward models being faithful proxies; the paper itself notes in Section 7 that 'DAPO rewards are imperfect proxies for clinical quality.'
  • domain assumption CXAS segmentation masks are accurate enough to support the measurement tools.
    The tool thresholds operate on widths/areas derived from CXAS masks. The paper admits in Section 7 that it did not independently validate CXAS quality on the NH cohort.
  • domain assumption The GPT-5.1-extracted Tubes & Lines placement labels used in training are accurate.
    Appendix E.1 uses GPT-5.1 to extract device status from radiology reports without reporting a validation of the extraction quality on the training data.
  • standard math DAPO is an appropriate RL algorithm for multi-task radiology optimization.
    The paper applies DAPO as an off-the-shelf method from Yu et al. 2025, citing its Clip-Higher and token-level normalization properties.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement." pith.science (2026). https://pith.science/paper/RKNGIOBP

@misc{pith2026260803890,
  author       = {Pith},
  title        = {Pith review of: CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RKNGIOBP}},
  note         = {Machine review of arXiv:2608.03890}
}
read the original abstract

A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive the anatomical measurements upon which many diagnoses depend. Today's Vision-Language Models (VLMs) treat these as separate problems, if they address them at all, leaving a gap between what radiologists need and what generative models provide. We introduce CARE-X, a chest X-ray VLM that narrows this gap by unifying auxiliary discriminative supervision with reward-aligned generation. CARE-X augments its generative backbone with focal-loss classification and composite-loss grounding heads, co-trained alongside the language-modeling objective. This auxiliary supervision produces discriminative diagnostic predictions with tunable decision thresholds and precise spatial localization while also improving report quality, providing evidence that structured prediction and generation reinforce one another. Building on this foundation, Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) leverages task-specific reward signals for report generation, visual question answering (VQA), and spatial grounding, directly optimizing the clinical quality metrics that matter in practice. The result is state-of-the-art performance on the majority of metrics across four report-generation benchmarks, 94.0% VQA accuracy on ReXVQA (+6.0 pp over the next-best baseline), and generative spatial decoding that reaches near parity with dedicated detection heads. Separately, to address measurement-dependent diagnoses, we couple Qwen3-VL-4B-Instruct with native tool-calling capabilities for invoking deterministic measurement tools, while retaining full visual access to the image. This hybrid inference yields +43.6 pp average F1 over perception-only baselines across five measurement-dependent conditions.

Figures

Figures reproduced from arXiv: 2608.03890 by the authors.

Figure 1
Figure 1. The CARE-X model. (Left) Supervised fine-tuning with three task-specific heads for classification, grounding, and report generation/VQA. (Right) DAPO with task-specific rewards. See Section 3.1.1 for details. Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO). In the second phase, we apply DAPO (Yu et al., 2025) to directly optimize task-specific reward signals that capture clinical quality dimensions. W… view at source ↗
Figure 2
Figure 2. Quantitative Reasoning Inference Pipeline [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. NH Dataset Prevalence Distribution for Inpatient & ICU Conditions [PITH_FULL_IMAGE:figures/full_fig_p030_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

182 extracted references · 24 canonical work pages

  1. [1]

    2026 , eprint=

    A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation , author=. 2026 , eprint=

    2026

  2. [2]

    2025 , url=

    Ranjit, Mercy Prasanna and Porya, Anirban and Srivastav, Shaury and Vadlamudi, Niharika and Eathamukkala, Nikhilesh Chowdary and Udyavar, Shashank and Kumar, Rahul and Ganu, Tanuja , booktitle=. 2025 , url=

    2025

  3. [3]

    2025 , eprint=

    Deperrois, Nicolas and Matsuo, Hidetoshi and Ruip. 2025 , eprint=

    2025

  4. [4]

    2026 , eprint=

    Scaling medical imaging report generation with multimodal reinforcement learning , author=. 2026 , eprint=

    2026

  5. [5]

    2025 , eprint=

    Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning , author=. 2025 , eprint=

    2025

  6. [6]

    GREEN : Generative Radiology Report Evaluation and Error Notation

    Ostmeier, Sophie and Xu, Justin and Chen, Zhihong and Varma, Maya and Blankemeier, Louis and Bluethgen, Christian and Michalson, Arne Edward and Moseley, Michael and Langlotz, Curtis and Chaudhari, Akshay S and Delbrouck, Jean-Benoit. GREEN : Generative Radiology Report Evaluation and Error Notation. Findings of the Association for Computational Linguisti...

    doi:10.18653/v1/2024.findings-emnlp.21 2024

  7. [7]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Sigmoid loss for language image pre-training , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  8. [8]

    Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen , booktitle=

    Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen , booktitle=. 2022 , url=

    2022

Show all 182 references
  1. [9]

    Weinberger and Yoav Artzi , booktitle=

    Tianyi Zhang and Varsha Kishore and Felix Wu and Kilian Q. Weinberger and Yoav Artzi , booktitle=. 2020 , url=

    2020

  2. [10]

    Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards

    Delbrouck, Jean-Benoit and Chambon, Pierre and Bluethgen, Christian and Tsai, Emily and Almusa, Omar and Langlotz, Curtis. Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards. Findings of the Association for Computational Linguistics: EMNLP 2...

    2022 doi

  3. [11]

    Topol and Pranav Rajpurkar , title =

    Hong-Yu Zhou and Julián Nicolás Acosta and Subathra Adithan and Suvrankar Datta and Eric J. Topol and Pranav Rajpurkar , title =. NEJM AI , volume =. 2026 , doi =. https://ai.nejm.org/doi/pdf/10.1056/AIoa2500595 , abstract =

    2026 doi

  4. [12]

    2503.14476 , archivePrefix=

    Qiying Yu and Zheng Zhang and Ruofei Zhu and Yufeng Yuan and Xiaochen Zuo and Yu Yue and Weinan Dai and Tiantian Fan and Gaohong Liu and Lingjun Liu and Xin Liu and Haibin Lin and Zhiqi Lin and Bole Ma and Guangming Sheng and Yuxuan Tong and Chi Zhang and Mofan Zhang and Wang ...

    arXiv

  5. [13]

    and Miller, Josh and Huang, Ouwen and Rajpurkar, Pranav , booktitle =

    Zhang, Xiaoman and Zhou, Hong-Yu and Yang, Xiaoli and Banerjee, Oishi and Acosta, Juli\'an N. and Miller, Josh and Huang, Ouwen and Rajpurkar, Pranav , booktitle =. 2025 , editor =

    2025

  6. [14]

    GitHub repository , howpublished =

    Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec , title =. GitHub repository , howpublished =. 2022 , publisher =

    2022

  7. [15]

    2026 , eprint=

    CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation , author=. 2026 , eprint=

    2026

  8. [16]

    2025 , eprint =

    Lee, Hyungyung and Choi, Geon and Lee, Jung-Oh and Yoon, Hangyul and Hong, Hyuk Gi and Choi, Edward , booktitle =. 2025 , eprint =

    2025

  9. [17]

    2602.23276 , archivePrefix =

    Lee, Hyungyung and Yoon, Hangyul and Choi, Edward , year =. 2602.23276 , archivePrefix =

  10. [18]

    arXiv preprint arXiv:2306.03934 , year =

    Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling , author =. arXiv preprint arXiv:2306.03934 , year =

    arXiv

  11. [19]

    Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren , journal =

  12. [20]

    and Gonzalez, Joseph and Zhang, Hao , booktitle =

    Kwon, Woosuk and Li, Zhuohan and Zhuang, Siyuan and Ying, Ying and Liang, Eric and Stoica, Ion and Xing, Eric P. and Gonzalez, Joseph and Zhang, Hao , booktitle =. Efficient Memory Management for Large Language Model Serving with

  13. [21]

    Guyon and A

    I. Guyon and A. Elisseeff. An Introduction to Variable and Feature Selection. JMLR

  14. [22]

    Guyon and C

    I. Guyon and C. Aliferis and A. Elisseeff , title =

  15. [23]

    and Mosquera, C

    Gaggion, N. and Mosquera, C. and Aineseder, M. and Mansilla, L. and Milone, D. and Ferrante, E. , title =. 2023 , howpublished =

    2023

  16. [24]

    2401.10815 , archivePrefix=

    P. 2401.10815 , archivePrefix=

    arXiv

  17. [25]

    arXiv preprint arXiv:2407.10671 , year=

    Qwen2 technical report , author=. arXiv preprint arXiv:2407.10671 , year=

    arXiv

  18. [26]

    arXiv preprint arXiv:2210.12186 , year=

    Improving the factual correctness of radiology report generation with semantic rewards , author=. arXiv preprint arXiv:2210.12186 , year=

    arXiv

  19. [27]

    Findings of the Association for Computational Linguistics ACL 2024 , pages=

    Radgraph-xl: A large-scale expert-annotated dataset for entity and relation extraction from radiology reports , author=. Findings of the Association for Computational Linguistics ACL 2024 , pages=

    2024

  20. [29]

    and others , title =

    Jackson, Valerie P. and others , title =. Journal of the American College of Radiology , volume =. 2009 , doi =

    2009

  21. [30]

    2025 , url =

    OpenAI , title =. 2025 , url =

    2025

  22. [31]

    arXiv preprint arXiv:2411.15122 , year=

    Rexrank: A public leaderboard for ai-powered radiology report generation , author=. arXiv preprint arXiv:2411.15122 , year=

    arXiv

  23. [32]

    1895 , publisher=

    Ueber eine neue Art von Strahlen , author=. 1895 , publisher=

  24. [33]

    Boosting Radiology Report Generation by Infusing Comparison Prior

    Kim, Sanghwan and Nooralahzadeh, Farhad and Rohanian, Morteza and Fujimoto, Koji and Nishio, Mizuho and Sakamoto, Ryo and Rinaldi, Fabio and Krauthammer, Michael. Boosting Radiology Report Generation by Infusing Comparison Prior. The 22nd Workshop on Biomedical Natural Languag...

    2023 doi

  25. [34]

    Nature Biomedical Engineering , pages=

    A vision--language foundation model for the generation of realistic chest x-ray images , author=. Nature Biomedical Engineering , pages=. 2024 , publisher=

    2024

  26. [35]

    arXiv preprint arXiv:2405.07960 , year=

    AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments , author=. arXiv preprint arXiv:2405.07960 , year=

    arXiv

  27. [36]

    Proceedings of the 18th NTCIR Conference on Evaluation of Information Access Technologies , YEAR =

    Overview of the NTCIR-18 HIDDEN-RAD Task: Hidden Causality Inclusion in Radiology Report Generation , author=. Proceedings of the 18th NTCIR Conference on Evaluation of Information Access Technologies , YEAR =

  28. [37]

    2024 , organization =

    Llama 3.3 70B: Model Card , author =. 2024 , organization =

    2024

  29. [38]

    2506.04353 , archivePrefix=

    Ankit Pal and Jung-Oh Lee and Xiaoman Zhang and Malaikannan Sankarasubbu and Seunghyeon Roh and Won Jung Kim and Meesun Lee and Pranav Rajpurkar , year=. 2506.04353 , archivePrefix=

    arXiv

  30. [39]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Deep residual learning for image recognition , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  31. [40]

    2019 , url =

    Johnson, Alistair EW and Pollard, Tom J and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng, Chih-ying and Peng, Yifan and Lu, Zhiyong and Mark, Roger G and Berkowitz, Seth J and Horng, Steven , journal=. 2019 , url =

    2019

  32. [41]

    arXiv preprint arXiv:2411.11362 , year=

    MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models , author=. arXiv preprint arXiv:2411.11362 , year=

    arXiv

  33. [42]

    arXiv preprint arXiv:2409.12191 , year=

    Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution , author=. arXiv preprint arXiv:2409.12191 , year=

    arXiv

  34. [43]

    Hidden-Rad Evaluation Scheme of NTCIR-18 , year =

    Cho, Yousang and Choi, Key-Sun and. Hidden-Rad Evaluation Scheme of NTCIR-18 , year =

  35. [44]

    Diagnostics , volume =

    Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review , author =. Diagnostics , volume =. 2025 , publisher =. doi:10.3390/diagnostics15091146 , url =

    2025 doi

  36. [45]

    Medical image analysis , volume=

    Bustos, Aurelia and Pertusa, Antonio and Salinas, Jose-Maria and de la Iglesia-Vay. Medical image analysis , volume=. 2020 , publisher=

    2020

  37. [46]

    arXiv preprint arXiv:2411.05085 , year=

    PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation , author=. arXiv preprint arXiv:2411.05085 , year=

    arXiv

  38. [47]

    arXiv preprint arXiv:2405.03595 , year=

    GREEN: Generative Radiology Report Evaluation and Error Notation , author=. arXiv preprint arXiv:2405.03595 , year=

    arXiv

  39. [48]

    European conference on computer vision , pages=

    Making the most of text semantics to improve biomedical vision--language processing , author=. European conference on computer vision , pages=. 2022 , organization=

    2022

  40. [49]

    European Conference on Computer Vision , pages=

    Any-resolution training for high-resolution image synthesis , author=. European Conference on Computer Vision , pages=. 2022 , organization=

    2022

  41. [50]

    European journal of radiology , volume=

    Radiologist burnout: trends in medical imaging utilization under the national health insurance system with the universal code bundling strategy in an academic tertiary medical centre , author=. European journal of radiology , volume=. 2022 , publisher=

    2022

  42. [51]

    Proceedings of the IEEE conference on computer vision and pattern recognition , year=

    You only look once: Unified, real-time object detection , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , year=

  43. [52]

    IEEE transactions on pattern analysis and machine intelligence , volume=

    Faster R-CNN: Towards real-time object detection with region proposal networks , author=. IEEE transactions on pattern analysis and machine intelligence , volume=. 2016 , publisher=

    2016

  44. [53]

    Nature , volume=

    Large language models encode clinical knowledge , author=. Nature , volume=. 2023 , publisher=

    2023

  45. [54]

    NEJM AI , volume=

    Towards generalist biomedical AI , author=. NEJM AI , volume=. 2024 , publisher=

    2024

  46. [55]

    Advances in Neural Information Processing Systems , year=

    Attention is all you need , author=. Advances in Neural Information Processing Systems , year=

  47. [56]

    arXiv preprint arXiv:2005.14165 , year=

    Language models are few-shot learners , author=. arXiv preprint arXiv:2005.14165 , year=

    2005 arXiv

  48. [58]

    OpenAI blog , volume=

    Language models are unsupervised multitask learners , author=. OpenAI blog , volume=

  49. [59]

    arXiv preprint arXiv:2109.01652 , year=

    Finetuned language models are zero-shot learners , author=. arXiv preprint arXiv:2109.01652 , year=

    arXiv

  50. [60]

    arXiv preprint arXiv:2303.08774v6 , year=

    Gpt-4 technical report , author=. arXiv preprint arXiv:2303.08774v6 , year=

    arXiv

  51. [61]

    arXiv preprint arXiv:2312.11805 , year=

    Gemini: a family of highly capable multimodal models , author=. arXiv preprint arXiv:2312.11805 , year=

    arXiv

  52. [62]

    International conference on machine learning , pages=

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models , author=. International conference on machine learning , pages=. 2023 , organization=

    2023

  53. [63]

    , author=

    Building and better understanding vision-language models: insights and future directions. , author=. 2024 , eprint=

    2024

  54. [64]

    Nature Biomedical Engineering , volume=

    Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning , author=. Nature Biomedical Engineering , volume=. 2022 , publisher=

    2022

  55. [65]

    BMC Medical Informatics and Decision Making , volume=

    BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning , author=. BMC Medical Informatics and Decision Making , volume=. 2024 , publisher=

    2024

  56. [66]

    2022 IEEE international conference on bioinformatics and biomedicine (BIBM) , pages=

    Research on chest abnormality detection based on improved YOLOv7 algorithm , author=. 2022 IEEE international conference on bioinformatics and biomedicine (BIBM) , pages=. 2022 , organization=

    2022

  57. [67]

    arXiv preprint arXiv:2310.07704 , year=

    Ferret: Refer and ground anything anywhere at any granularity , author=. arXiv preprint arXiv:2310.07704 , year=

    arXiv

  58. [68]

    arXiv preprint arXiv:2404.07973 , year=

    Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models , author=. arXiv preprint arXiv:2404.07973 , year=

    arXiv

  59. [69]

    arXiv preprint arXiv:2212.10560 , year=

    Self-instruct: Aligning language models with self-generated instructions , author=. arXiv preprint arXiv:2212.10560 , year=

    arXiv

  60. [70]

    Foundations and Trends

    Multimodal foundation models: From specialists to general-purpose assistants , author=. Foundations and Trends. 2024 , publisher=

    2024

  61. [71]

    arXiv preprint arXiv:2309.11998 , year=

    Lmsys-chat-1m: A large-scale real-world llm conversation dataset , author=. arXiv preprint arXiv:2309.11998 , year=

    arXiv

  62. [72]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Vila: On pre-training for visual language models , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  63. [73]

    Advances in Neural Information Processing Systems , volume=

    Judging llm-as-a-judge with mt-bench and chatbot arena , author=. Advances in Neural Information Processing Systems , volume=

  64. [74]

    arXiv preprint arXiv:2211.05719 , year=

    Mmdialog: A large-scale multi-turn dialogue dataset towards multi-modal open-domain conversation , author=. arXiv preprint arXiv:2211.05719 , year=

    arXiv

  65. [75]

    2015 IEEE international conference on bioinformatics and biomedicine (BIBM) , pages=

    Foreign object detection in chest X-rays , author=. 2015 IEEE international conference on bioinformatics and biomedicine (BIBM) , pages=. 2015 , organization=

    2015

  66. [76]

    arXiv preprint arXiv:2404.15770 , year=

    ChEX: Interactive Localization and Region Description in Chest X-rays , author=. arXiv preprint arXiv:2404.15770 , year=

    arXiv

  67. [77]

    Image and Vision Computing , volume=

    Weighted boxes fusion: Ensembling boxes from different object detection models , author=. Image and Vision Computing , volume=. 2021 , publisher=

    2021

  68. [78]

    arXiv preprint arXiv:2004.09167 , year=

    CheXbert: combining automatic labelers and expert annotations for accurate radiology report labeling using BERT , author=. arXiv preprint arXiv:2004.09167 , year=

    2004 arXiv

  69. [79]

    Langlotz , year=

    Pierre Chambon and Jean-Benoit Delbrouck and Thomas Sounack and Shih-Cheng Huang and Zhihong Chen and Maya Varma and Steven QH Truong and Chu The Chuong and Curtis P. Langlotz , year=. 2405.19538 , archivePrefix=

    arXiv

  70. [80]

    arXiv preprint arXiv:2408.16213 , year=

    M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation , author=. arXiv preprint arXiv:2408.16213 , year=

    arXiv

  71. [81]

    arXiv preprint arXiv:2403.15456 , year=

    WoLF: Large Language Model Framework for CXR Understanding , author=. arXiv preprint arXiv:2403.15456 , year=

    arXiv

  72. [82]

    International Conference on Machine Learning , pages=

    Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework , author=. International Conference on Machine Learning , pages=. 2022 , organization=

    2022

  73. [83]

    Text summarization branches out , pages=

    Rouge: A package for automatic evaluation of summaries , author=. Text summarization branches out , pages=

  74. [84]

    arXiv preprint arXiv:2311.13668 , year=

    Maira-1: A specialised large multimodal model for radiology report generation , author=. arXiv preprint arXiv:2311.13668 , year=

    arXiv

  75. [85]

    Medical Image Computing and Computer Assisted Intervention--MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I , pages=

    Multimodal recurrent model with attention for automated radiology report generation , author=. Medical Image Computing and Computer Assisted Intervention--MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I , pages=. 2018 , or...

    2018

  76. [86]

    Progressive Transformer-Based Generation of Radiology Reports

    Nooralahzadeh, Farhad and Perez Gonzalez, Nicolas and Frauenfelder, Thomas and Fujimoto, Koji and Krauthammer, Michael. Progressive Transformer-Based Generation of Radiology Reports. Findings of the Association for Computational Linguistics: EMNLP 2021. 2021. doi:10.18653/v1/2...

    2021 doi

  77. [87]

    Informatics in Medicine Unlocked , volume=

    Automated radiology report generation using conditioned transformers , author=. Informatics in Medicine Unlocked , volume=. 2021 , publisher=

    2021

  78. [88]

    arXiv preprint arXiv:2408.11556 , year=

    Understanding data movement in tightly coupled heterogeneous systems: A case study with the Grace Hopper superchip , author=. arXiv preprint arXiv:2408.11556 , year=

    arXiv

  79. [89]

    Papineni, Kishore and Roukos, Salim and Ward, Todd and Zhu, Wei-Jing , booktitle=

  80. [90]

    International Conference on Learning Representations , year=

    BERTScore: Evaluating Text Generation with BERT , author=. International Conference on Learning Representations , year=

  81. [91]

    arXiv preprint arXiv:2408.03326 , year=

    LLaVA-OneVision: Easy Visual Task Transfer , author=. arXiv preprint arXiv:2408.03326 , year=

    arXiv

  82. [92]

    2024 , eprint=

    Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation , author=. 2024 , eprint=

    2024

  83. [93]

    arXiv preprint arXiv:2404.18416 , year=

    Capabilities of gemini models in medicine , author=. arXiv preprint arXiv:2404.18416 , year=

    arXiv

  84. [94]

    arXiv preprint arXiv:2405.03162 , year=

    Advancing multimodal medical capabilities of Gemini , author=. arXiv preprint arXiv:2405.03162 , year=

    arXiv

  85. [95]

    arXiv preprint arXiv:2405.02246 , year=

    What matters when building vision-language models? , author=. arXiv preprint arXiv:2405.02246 , year=

    arXiv

  86. [96]

    Advances in neural information processing systems , volume=

    Training language models to follow instructions with human feedback , author=. Advances in neural information processing systems , volume=

  87. [97]

    arXiv preprint arXiv:2304.03277 , year=

    Instruction tuning with gpt-4 , author=. arXiv preprint arXiv:2304.03277 , year=

    arXiv

  88. [98]

    Patterns , volume=

    Evaluating progress in automatic chest x-ray radiology report generation , author=. Patterns , volume=. 2023 , publisher=

    2023

  89. [99]

    2024 , eprint=

    InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks , author=. 2024 , eprint=

    2024

  90. [100]

    arXiv preprint arXiv:2305.10415 , year=

    Pmc-vqa: Visual instruction tuning for medical visual question answering , author=. arXiv preprint arXiv:2305.10415 , year=

    arXiv

  91. [101]

    arXiv preprint arXiv:2211.08584 , year=

    Toward expanding the scope of radiology report summarization to multiple anatomies and modalities , author=. arXiv preprint arXiv:2211.08584 , year=

    arXiv

  92. [102]

    Journal of Computing Science and Engineering , volume=

    Design and development of a multimodal biomedical information retrieval system , author=. Journal of Computing Science and Engineering , volume=. 2012 , publisher=

    2012

  93. [103]

    Quantitative imaging in medicine and surgery , volume=

    Two public chest X-ray datasets for computer-aided screening of pulmonary diseases , author=. Quantitative imaging in medicine and surgery , volume=. 2014 , publisher=

    2014

  94. [104]

    2023 , publisher=

    MSCXR-T: Learning to exploit temporal structure for biomedical vision-language processing , author=. 2023 , publisher=

    2023

  95. [105]

    International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=

    Rad-restruct: A novel vqa benchmark and method for structured radiology reporting , author=. International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=. 2023 , organization=

    2023

  96. [106]

    Scientific data , volume=

    A dataset of clinically generated visual questions and answers about radiology images , author=. Scientific data , volume=. 2018 , publisher=

    2018

  97. [107]

    Scientific Data , volume=

    PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children , author=. Scientific Data , volume=. 2023 , publisher=

    2023

  98. [108]

    VinDr-CXR: An open dataset of chest X-rays with radiologists

    Nguyen, Ha Q and Lam, Khanh and Le, Linh T and Pham, Hieu H and Tran, Dat Q and Nguyen, Dung B and Le, Dung D and Pham, Chi M and Tong, Hang TT and Dinh, Diep H and others , journal=. VinDr-CXR: An open dataset of chest X-rays with radiologists. 2022 , publisher=

    2022

  99. [109]

    Radiology: Artificial Intelligence , volume=

    Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia , author=. Radiology: Artificial Intelligence , volume=. 2019 , publisher=

    2019

  100. [110]

    Radiology Objects in COntext (ROCO): a multimodal image dataset , author=. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis: 7th Joint International Workshop, CVII-STENT 2018 and Third International W...

    2018

  101. [111]

    International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=

    Explaining chest x-ray pathologies in natural language , author=. International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=. 2022 , organization=

    2022

  102. [112]

    Advances in Neural Information Processing Systems , volume=

    EHRXQA: A multi-modal question answering dataset for electronic health records with chest x-ray images , author=. Advances in Neural Information Processing Systems , volume=

  103. [113]

    Medical-Diff-VQA: A Large-Scale Medical Dataset for Difference Visual Question Answering on Chest X-Ray Images , author=

  104. [114]

    Proceedings of CLEF (Conference and Labs of the Evaluation Forum) 2019 Working Notes , year=

    Vqa-med: Overview of the medical visual question answering task at imageclef 2019 , author=. Proceedings of CLEF (Conference and Labs of the Evaluation Forum) 2019 Working Notes , year=

    2019

  105. [115]

    2023 , note=

    Holste, Gregory and Wang, Song and Jaiswal, Ajay and Yang, Yuzhe and Lin, Mingquan and Peng, Yifan and Wang, Atlas , journal=. 2023 , note=. doi:10.13026/721s-vs37 , url=

    2023 doi

  106. [116]

    2019 , doi=

    Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others , booktitle=. 2019 , doi=

    2019

  107. [117]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  108. [118]

    Curation of the

    Feng, Sijing and Azzollini, Damian and Kim, Ji Soo and Jin, Cheng-Kai and Gordon, Simon P and Yeoh, Jason and Kim, Eve and Han, Mina and Lee, Andrew and Patel, Aakash and others , journal=. Curation of the. 2021 , publisher=

    2021

  109. [119]

    Scientific Data , volume=

    BRAX, Brazilian labeled chest x-ray dataset , author=. Scientific Data , volume=. 2022 , publisher=

    2022

  110. [120]

    arXiv preprint arXiv:2006.01174 , year=

    Vay. arXiv preprint arXiv:2006.01174 , year=

    2006 arXiv

  111. [121]

    arXiv preprint arXiv:2306.07971 , year=

    Xraygpt: Chest radiographs summarization using medical vision-language models , author=. arXiv preprint arXiv:2306.07971 , year=

    arXiv

  112. [122]

    arXiv preprint arXiv:2210.10163 , year=

    Medclip: Contrastive learning from unpaired medical images and text , author=. arXiv preprint arXiv:2210.10163 , year=

    arXiv

  113. [123]

    Advances in Neural Information Processing Systems , volume=

    Lamm: Language-assisted multi-modal instruction-tuning dataset, framework, and benchmark , author=. Advances in Neural Information Processing Systems , volume=

  114. [124]

    arXiv preprint arXiv:2306.00890 , year=

    Llava-med: Training a large language-and-vision assistant for biomedicine in one day , author=. arXiv preprint arXiv:2306.00890 , year=

    arXiv

  115. [125]

    arXiv preprint arXiv:2212.10773 , year=

    Multiinstruct: Improving multi-modal zero-shot learning via instruction tuning , author=. arXiv preprint arXiv:2212.10773 , year=

    arXiv

  116. [126]

    arXiv preprint arXiv:2310.06825 , year=

    Mistral 7B , author=. arXiv preprint arXiv:2310.06825 , year=

    arXiv

  117. [127]

    arXiv preprint arXiv:2304.08485 , year=

    Visual instruction tuning , author=. arXiv preprint arXiv:2304.08485 , year=

    arXiv

  118. [128]

    Wu, Joy T and Agu, Nkechinyere N and Lourentzou, Ismini and Sharma, Arjun and Paguio, Joseph A and Yao, Jasper S and Dee, Edward C and Mitchell, William and Kashyap, Satyananda and Giovannini, Andrea and others , booktitle=. Chest. 2021 , url=

    2021

  119. [129]

    arXiv preprint arXiv:2106.14463 , year=

    Radgraph: Extracting clinical entities and relations from radiology reports , author=. arXiv preprint arXiv:2106.14463 , year=

    arXiv

  120. [130]

    Advances in neural information processing systems , volume=

    Faster r-cnn: Towards real-time object detection with region proposal networks , author=. Advances in neural information processing systems , volume=

  121. [131]

    European Conference on Computer Vision , pages=

    Unitab: Unifying text and box outputs for grounded vision-language modeling , author=. European Conference on Computer Vision , pages=. 2022 , organization=

    2022

  122. [132]

    arXiv preprint arXiv:2109.10852 , year=

    Pix2seq: A language modeling framework for object detection , author=. arXiv preprint arXiv:2109.10852 , year=

    arXiv

  123. [133]

    arXiv preprint arXiv:2010.12812 , year=

    A frustratingly easy approach for entity and relation extraction , author=. arXiv preprint arXiv:2010.12812 , year=

    2010 arXiv

  124. [134]

    arXiv preprint arXiv:2311.18681 , year=

    RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance , author=. arXiv preprint arXiv:2311.18681 , year=

    arXiv

  125. [135]

    Image Pivoting for Learning Multilingual Multimodal Representations

    Gella, Spandana and Sennrich, Rico and Keller, Frank and Lapata, Mirella. Image Pivoting for Learning Multilingual Multimodal Representations. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017. doi:10.18653/v1/D17-1303

    2017 doi

  126. [136]

    A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking

    Nakayama, Hideki and Tamura, Akihiro and Ninomiya, Takashi. A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking. Proceedings of the Twelfth Language Resources and Evaluation Conference. 2020

    2020

  127. [137]

    C ross2 S tr A : Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment

    Wu, Shengqiong and Fei, Hao and Ji, Wei and Chua, Tat-Seng. C ross2 S tr A : Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: L...

    2023 doi

  128. [138]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Interactive and Explainable Region-guided Radiology Report Generation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  129. [139]

    2023 , eprint=

    EVA-CLIP: Improved Training Techniques for CLIP at Scale , author=. 2023 , eprint=

    2023

  130. [140]

    2024 , eprint=

    EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters , author=. 2024 , eprint=

    2024

  131. [141]

    arXiv preprint arXiv:2401.13601 , year=

    Mm-llms: Recent advances in multimodal large language models , author=. arXiv preprint arXiv:2401.13601 , year=

    arXiv

  132. [142]

    Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models

    Huang, Po-Yao and Patrick, Mandela and Hu, Junjie and Neubig, Graham and Metze, Florian and Hauptmann, Alexander. Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models. Proceedings of the 2021 Conference of the North American Chapt...

    2021 doi

  133. [143]

    Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X -rays

    Ni, Jianmo and Hsu, Chun-Nan and Gentili, Amilcare and McAuley, Julian. Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X -rays. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020. doi:10.18653/v1/2020.findings-emnlp.176

    2020 doi

  134. [144]

    Mettler and Mythreyi Bhargavan and Keith Faulkner and Debbie B

    Fred A. Mettler and Mythreyi Bhargavan and Keith Faulkner and Debbie B. Gilley and Joel E. Gray and Geoffrey S. Ibbott and Jill A. Lipoti and Mahadevappa Mahesh and John L. McCrohan and Michael G. Stabin and Bruce R. Thomadsen and Terry T. Yoshizumi , doi =. Radiologic and Nuc...

    1950 doi

  135. [145]

    Chan and Shyh Jye Chen , doi =

    Yan Chih Peng and Wen Jeng Lee and Yeun Chung Chang and Wing P. Chan and Shyh Jye Chen , doi =. Radiologist burnout: Trends in medical imaging utilization under the national health insurance system with the universal code bundling strategy in an academic tertiary medical centr...

  136. [146]

    Alsaif , doi =

    Malak Al Shammari and Ali Hassan and Nouf AlShamlan and Sarah Alotaibi and Manar Bamashmoos and Amani Hakami and Abdullatif Althunyan and Shymaa Basager and Sameerah Motabgani and Sawsan Aljubran and Hind S. Alsaif , doi =. Family medicine residents’ skill levels in emergency ...

  137. [147]

    Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods , volume =

    Mizuho Nishio and Shunjiro Noguchi and Hidetoshi Matsuo and Takamichi Murakami , doi =. Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods , volume =. Scientific Reports 20...

    2020

  138. [148]

    NIH , title =. n.d. , url =

  139. [149]

    An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study , volume =

    Fatemeh Homayounieh and Subba Digumarthy and Shadi Ebrahimian and Johannes Rueckel and Boj Friedrich Hoppe and Bastian Oliver Sabel and Sailesh Conjeti and Karsten Ridder and Markus Sistermanns and Lei Wang and Alexander Preuhs and Florin Ghesu and Awais Mansoor and Mateen Mog...

  140. [150]

    Kevin Zhou and Li Xiao , doi =

    Shuxin Yang and Xian Wu and Shen Ge and Zhuozhao Zheng and S. Kevin Zhou and Li Xiao , doi =. Radiology report generation with a learned knowledge base and multi-modal alignment , volume =. Medical Image Analysis , keywords =

  141. [151]

    Towards Conversational Diagnostic AI

    Tu, Tao and Palepu, Anil and Schaekermann, Mike and Saab, Khaled and Freyberg, Jan and Tanno, Ryutaro and Wang, Amy and Li, Brenna and Amin, Mohamed and Tomasev, Nenad and Azizi, Shekoofeh and Singhal, Karan and Cheng, Yong and Hou, Le and Webson, Albert and Kulkarni, Kavita a...

  142. [152]

    ChatGPT Can Now See , Hear , and Speak

    OpenAI. ChatGPT Can Now See , Hear , and Speak. 2024

    2024

  143. [153]

    Introducing the Next Generation of Claude

    Anthropic. Introducing the Next Generation of Claude. 2024

    2024

  144. [154]

    Large language models encode clinical knowledge

    Singhal, Karan and Azizi, Shekoofeh and Tu, Tao and Mahdavi, S Sara and Wei, Jason and Chung, Hyung Won and Scales, Nathan and Tanwani, Ajay and Cole-Lewis, Heather and Pfohl, Stephen and Payne, Perry and Seneviratne, Martin and Gamble, Paul and Kelly, Chris and Babiker, Abuba...

  145. [155]

    2024 , eprint=

    A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation , author=. 2024 , eprint=

    2024

  146. [156]

    RaDialog : A large vision-language model for radiology report generation and conversational assistance

    Pellegrini, Chantal and Özsoy, Ege and Busam, Benjamin and Navab, Nassir and Keicher, Matthias. RaDialog : A large vision-language model for radiology report generation and conversational assistance. arXiv [cs.CV]

  147. [157]

    MAIRA -2: Grounded Radiology Report Generation

    Bannur, Shruthi and Bouzid, Kenza and Castro, Daniel C and Schwaighofer, Anton and Thieme, Anja and Bond-Taylor, Sam and Ilse, Maximilian and Pérez-García, Fernando and Salvatelli, Valentina and Sharma, Harshita and Meissen, Felix and Ranjit, Mercy and Srivastav, Shaury and Go...

  148. [158]

    Frontiers in Big Data , volume=

    AI-based radiodiagnosis using chest X-rays: A review , author=. Frontiers in Big Data , volume=. 2023 , publisher=

    2023

  149. [159]

    Medical Image Analysis , volume=

    Deep learning for chest X-ray analysis: A survey , author=. Medical Image Analysis , volume=. 2021 , publisher=

    2021

  150. [160]

    arXiv preprint arXiv:2412.19437 , year=

    Deepseek-v3 technical report , author=. arXiv preprint arXiv:2412.19437 , year=

    arXiv

  151. [161]

    arXiv preprint arXiv:2507.05201 , year=

    Sellergren, Andrew and Kazemzadeh, Sahar and Jaroensri, Tiam and Kiraly, Atilla and Traverse, Madeleine and Kohlberger, Timo and Xu, Shawn and Jamil, Fayaz and Hughes, C. arXiv preprint arXiv:2507.05201 , year=

    arXiv

  152. [162]

    arXiv preprint arXiv:2508.08224 , year=

    Capabilities of gpt-5 on multimodal medical reasoning , author=. arXiv preprint arXiv:2508.08224 , year=

    arXiv

  153. [163]

    arXiv preprint arXiv:2406.04449 , year=

    Maira-2: Grounded radiology report generation , author=. arXiv preprint arXiv:2406.04449 , year=

    arXiv

  154. [164]

    arXiv preprint arXiv:2501.12948 , year=

    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning , author=. arXiv preprint arXiv:2501.12948 , year=

    arXiv

  155. [165]

    Radiology Report Generation Models Evaluation Dataset For Chest X-rays ( RadEvalX )

    Calamida, Amos Rubin and Nooralahzadeh, Farhad and Rohanian, Morteza and Nishio, Mizuho and Fujimoto, Koji and Krauthammer, Michael. Radiology Report Generation Models Evaluation Dataset For Chest X-rays ( RadEvalX )

  156. [166]

    Radiology Report Expert Evaluation ( ReXVal ) Dataset

    Yu, Feiyang and Endo, Mark and Krishnan, Rayan and Pan, Ian and Tsai, Andy and Reis, Eduardo Pontes and Kaiser Ururahy Nunes Fonseca, Eduardo and Lee, Henrique and Shakeri, Zahra and Ng, Andrew and Langlotz, Curtis and Venugopal, Vasantha Kumar and Rajpurkar, Pranav. Radiology...

  157. [167]

    arXiv preprint arXiv:2311.16764 , year=

    Radiology-Aware Model-Based Evaluation Metric for Report Generation , author=. arXiv preprint arXiv:2311.16764 , year=

    arXiv

  158. [168]

    arXiv preprint arXiv:2405.07988 , year =

    A Generalist Learner for Multifaceted Medical Image Interpretation , author =. arXiv preprint arXiv:2405.07988 , year =. 2405.07988 , archivePrefix=

    arXiv

  159. [169]

    2025 , eprint=

    SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features , author=. 2025 , eprint=

    2025

  160. [170]

    2024 , eprint=

    Phi-4 Technical Report , author=. 2024 , eprint=

    2024

  161. [171]

    2025 , eprint=

    Zhang, Xiaoman and Acosta, Juli. 2025 , eprint=

    2025

  162. [172]

    and Modi, Ankit and Lloyd, Robert and Hopkins, Benjamin and Langlotz, Curtis and Delbrouck, Jean-Benoit

    Xu, Justin and Chen, Zhihong and Johnston, Andrew and Blankemeier, Louis and Varma, Maya and Hom, Jason and Collins, William J. and Modi, Ankit and Lloyd, Robert and Hopkins, Benjamin and Langlotz, Curtis and Delbrouck, Jean-Benoit. Overview of the First Shared Task on Clinica...

    2024 doi

  163. [173]

    Journal of the American Medical Informatics Association , volume=

    Preparing a collection of radiology examinations for distribution and retrieval , author=. Journal of the American Medical Informatics Association , volume=. 2016 , publisher=

    2016

  164. [174]

    2025 , eprint=

    Anatomical grounding pre-training for medical phrase grounding , author=. 2025 , eprint=

    2025

  165. [175]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Mdetr-modulated detection for end-to-end multi-modal understanding , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  166. [176]

    2015 , eprint=

    U-Net: Convolutional Networks for Biomedical Image Segmentation , author=. 2015 , eprint=

    2015

  167. [177]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Generalized intersection over union: A metric and a loss for bounding box regression , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  168. [178]

    2020 , organization=

    Abedalla, Ayat and Abdullah, Malak and Al-Ayyoub, Mahmoud and Benkhelifa, Elhadj , booktitle=. 2020 , organization=

    2020

  169. [179]

    , title=

    Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M. , title=. Proceedings of the. 2017 , doi=

    2017

  170. [180]

    2020 , howpublished =

    Jarrel Seah and Jen and Maggie and Meng Law and Phil Culliton and Sarah Dowd , title =. 2020 , howpublished =

    2020

  171. [181]

    2025 , url =

    Bai, Shuai and Cai, Yuxuan and Chen, Ruizhe and Chen, Keqin and Chen, Xionghui and Cheng, Zesen and Deng, Lianghao and Ding, Wei and Gao, Chang and Ge, Chunjiang and others , journal=. 2025 , url =

    2025

  172. [182]

    Saquib Sarfraz and Matthias A

    Constantin Marc Seibold and Simon Reiß and M. Saquib Sarfraz and Matthias A. Fink and Victoria Mayer and Jan Sellner and Moon Sung Kim and Klaus H. Maier-Hein and Jens Kleesiek and Rainer Stiefelhagen , title =. 33rd British Machine Vision Conference 2022,. 2022 , url =. 2210....

    2022 arXiv

  173. [183]

    Nguyen and Khanh Lam and Linh T

    Ha Q. Nguyen and Khanh Lam and Linh T. Le and Hieu H. Pham and Dat Q. Tran and Dung B. Nguyen and Dung D. Le and Chi M. Pham and Hang T. T. Tong and Diep H. Dinh and Cuong D. Do and Luu T. Doan and Cuong N. Nguyen and Binh T. Nguyen and Que V. Nguyen and Au D. Hoang and Hien N...

    2022

  174. [184]

    2024 , eprint=

    DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models , author=. 2024 , eprint=

    2024

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.