REVIEW 13 cited by
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophisticated feature engineering based on medical domain knowledge or manual annotations by experts. In this work, we introduce a BERT-based approach to medical image report labeling that exploits both the scale of available rule-based systems and the quality of expert annotations. We demonstrate superior performance of a biomedically pretrained BERT model first trained on annotations of a rule-based labeler and then finetuned on a small set of expert annotations augmented with automated backtranslation. We find that our final model, CheXbert, is able to outperform the previous best rules-based labeler with statistical significance, setting a new SOTA for report labeling on one of the largest datasets of chest x-rays.
Forward citations
Cited by 13 Pith papers
-
AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation
AnatomiX, a two-stage anatomy-first multimodal LLM for chest X-ray interpretation, reports >25% relative gains on anatomy grounding and grounded captioning, but some aggregate benchmark numbers are internally inconsis...
-
CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
A chest X-ray VLM co-trained with classification and grounding heads, tuned with DAPO reinforcement learning, and augmented with deterministic measurement tools outperforms prior radiology VLMs on report generation, V...
-
NeuroMosaic: Anatomically Grounded Multimodal Large Language Modeling for Molecularly Aware Glioma Reasoning from 3D MRI and Clinical Narratives
NeuroMosaic links MRI regions to diagnostic language via an anatomical graph router and concept memory, reporting external macro-F1 up to 0.784, IDH AUROC 0.918, and 0.703 pointing accuracy, with a 0.036 macro-F1 gain...
-
Scaling medical imaging report generation with multimodal reinforcement learning
UniRG-CXR, a Qwen3-VL-8B model trained with SFT plus GRPO reinforcement learning that directly optimizes the ReXrank metric components, reports state-of-the-art 1/RadCliQ-v1 results on all four ReXrank chest X-ray dat...
-
Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays
Domain-adapted LLM encoders trained with masked token prediction and supervised contrastive learning improve chest X-ray image-text retrieval and external generalization, reaching GREEN scores of 0.308 on MIMIC-CXR an...
-
Interpreting Radiologist's Intention from Eye Movements in Chest X-ray Diagnosis
RadGazeIntent, a transformer model, predicts per-fixation diagnostic intention from radiologist gaze on chest X-rays, evaluated on three newly constructed intention-labeled datasets.
-
Learnable Retrieval Enhanced Visual-Text Alignment and Fusion for Radiology Report Generation
REVTAF, a retrieval-augmented radiology report generator, reports average gains of 7.4 points on MIMIC-CXR and 2.9 points on IU X-Ray across nine metrics.
-
Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical Reasoning
A new 8-stage chest X-ray VQA benchmark and a context-aware model trained on it.
-
Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis
Medical CLIP training with text, clinical, and graph soft labels plus negation hard negatives improves chest X-ray zero-shot and fine-tuned performance.
-
RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
RadReason trains a 7B language model with GRPO to output six radiology error sub-scores plus textual reasons, reporting Kendall tau 0.730 on ReXVal, best among offline metrics.
-
RadEyeVideo: Enhancing general-domain Large Vision Language Model for chest X-ray analysis with video representations of eye gaze
A video-based eye-gaze prompt improved report generation and diagnosis for one general-purpose vision-language model, LLaVA-OneVision, but hurt or barely helped two others, and the main comparison to medical models re...
-
Look & Mark: Leveraging Radiologist Eye Fixations and Bounding boxes in Multimodal Large Language Models for Chest X-ray Report Generation
Prompting multimodal LLMs with ground-truth bounding boxes and gaze durations improves chest X-ray report metrics, but the effect is inconsistent and relies on privileged annotations.
-
From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine
A PRISMA-ScR scoping review of 144 studies finds the field shifting from text-only LLMs to multimodal AI in medicine, with evaluation and data diversity still the main bottlenecks.
Discussion (0). Continue with ORCID to comment.