Pith. sign in

REVIEW 3 major objections 5 minor 31 references

VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read A vessel-following OCTA model jointly predicts 10-year CVD risk and four blood-factor conditions, beating general and ophthalmologic baselines.

desk verdict Useful new OCTA dataset and a sensible multi-task framing, but the module ablation is broken: the Table 4 baseline is VisionFM, not the Mamba backbone the model is built on, so the central claim is currently unsupported. read the letter →

arxiv 2507.20017 v1 pith:KNFRTQ3R submitted 2025-07-26 cs.CV

Lehan Wang , Hualiang Wang , Chubin Ou , Lushi Chen , Yunyi Liang , Xiaomeng Li This is my paper
classification cs.CV
keywords OCTAcardiovasculardiseaseriskpredictionMambavesseltrajectorymorphologymultimodallanguagemodelretinalimagingOCTA-CVDdataset
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

Retinal vessels mirror coronary circulation, and OCT angiography shows them in far more detail than fundus photos or OCT. The paper's claim is that a model can use that detail to do more than binary high/low CVD risk: VAMPIRE jointly predicts 10-year CVD risk and four CVD-related conditions (high blood glucose, high blood cholesterol, high blood triglycerides, and high blood pressure). To make this work, the authors introduce the OCTA-CVD dataset of 1,659 en face projection images from 843 patients and a two-part architecture that follows vascular trajectories and injects vessel-morphology descriptions. The paper reports that VAMPIRE surpasses standard CNN and ViT backbones, OCTA-based detection models, and fine-tuned ophthalmologic foundation models, with the advantage also appearing on a second hospital's 765 images. If the results hold, non-invasive OCTA screening can give patients actionable blood-factor warnings rather than a single risk label.

What carries the argument

The load-bearing object is the vessel-following scanning order inside a Mamba (selective state-space) backbone. Instead of raster or diagonal patch scans, the MBD module takes a SAM-OCTA vessel segmentation map, filters noise, runs depth-first search over the vessel tree, and concatenates patch blocks along each branch, with background patches inserted between branches, yielding $S = \operatorname{concat}_{i,j \in T}[V_i; B_{i,j}; V_j]$. This gives the state-space model a sequence whose local order is vascular continuity. The second component, the IEM module, computes a disease-diagnosis prompt from an OCTA-500-trained classifier, asks a multimodal language model for vessel-morphology descriptions, encodes them with a frozen text encoder, and fuses them into image features with cross-attention. The two modules together are what the ablation study credits for the performance gain.

What would settle it

Run VAMPIRE with the vessel-following DFS scan replaced by a fixed raster or diagonal scan, keeping the IEM module and all training settings identical: the paper's mechanism predicts a clear drop in F1 and AUPR, so an experiment showing no such drop would falsify the claim that vascular trajectory order is what drives the gain.

Watch

Extended reading notes

Core claim

The central discovery is that vascular direction and vessel morphology, extracted explicitly from OCTA en face images, are stronger predictors of CVD risk factors than generic image features learned by general-purpose backbones. VAMPIRE's Mamba-Based Directional module uses a pre-trained vessel segmentation to build a scanning sequence $S = \operatorname{concat}_{i,j \in T}[V_i; B_{i,j}; V_j]$ that follows continuous vessel branches, so each patch is contextualized along a real vascular trajectory; its Information-Enhanced Morphological module adds text descriptions of vessel shape, generated from a disease classifier and a multimodal language model, through cross-attention. The paper argues these two signals are complementary, and its experiments on the new OCTA-CVD dataset report F1 0.627, AUPR 0.647, and AUC 0.824, above fine-tuned ophthalmologic foundation models, with consistent AUC gains across all five risk categories on an external hospital set.

Load-bearing premise

The weakest load-bearing premise is that the SAM-OCTA segmentation maps are reliable enough that depth-first traversal yields clean, continuous vessel trajectories; if the maps are fragmented or noisy, the vessel-following scan order will not match real vascular anatomy and the MBD module's contribution will shrink.

Editorial extensions

If this is right

  • Jointly predicting four blood-factor conditions alongside 10-year CVD risk improves the F1 score by more than 4% over binary-only risk classification, so clinical screening can report which factor is abnormal, not just high/low risk.
  • On OCTA-CVD, VAMPIRE reaches F1 0.627, AUPR 0.647, and AUC 0.824, beating CNN backbones, OCTA-specific detectors, and fine-tuned ophthalmologic foundation models.
  • The ablation shows each module alone lifts F1 and AUPR, and both together give the best result, implying trajectory and morphology are complementary signals.
  • The improvement transfers to a second hospital's 765 images, where VAMPIRE achieves the highest AUC on all five categories against ViT, RETFound, and VisionFM.
  • Adding the IEM module to other backbones improves their performance too, so vessel-shape descriptions act as portable prior knowledge rather than being tied to VAMPIRE's architecture.

Reading between the lines

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

  • The same vessel-following Mamba scan could transfer to other tubular-structure medical images, such as coronary angiography or OCTA-based diabetic retinopathy, where continuity along a branch is the informative axis.
  • Ablating the MLLM-generated text with fixed, hand-written vessel-shape templates would isolate whether the gain comes from semantic morphology knowledge or from the extra text-conditioning signal itself.
  • Because OCTA-CVD skews female (74.3%) and young (mean age 45), a demographic subgroup analysis on older or male-heavy cohorts would test whether the reported margins persist in the populations most at risk.
  • The joint-task framing suggests any vascular biomarker label, such as vessel caliber, could serve as an auxiliary task; testing additional auxiliary outputs would reveal whether the benefit is specific to blood-factor labels or a general multi-task effect.
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 / 5 minor

Summary. The paper proposes VAMPIRE, a multi-task deep learning framework that jointly predicts 10-year cardiovascular disease (CVD) risk and four CVD-related conditions (high blood glucose, cholesterol, triglycerides, and hypertension) from OCTA enface images. The framework combines a Mamba-Based Directional (MBD) module, which uses vessel segmentation maps and depth-first search to define a vessel-following scanning order for the state-space model, and an Information-Enhanced Morphological (IEM) module, which integrates textual vessel-shape descriptions generated by prompting GPT-4o with the outputs of a disease classifier. The authors introduce a new in-house dataset, OCTA-CVD, of 1,659 images from 843 patients, and report experiments with five-fold cross-validation and a second-hospital extension. The main claimed results are improvements over CNN backbones, OCTA-specific detection methods, and fine-tuned ophthalmologic foundation models on F1 score, AUC, and AUPR.

Significance. If the central claims were supported, the paper would make a useful contribution in at least three respects: it releases a first-of-its-kind OCTA dataset for CVD risk assessment; it introduces a multi-task paradigm that goes beyond binary risk classification and aligns with clinical blood-panel factors; and it proposes a vessel-aware scanning mechanism for Mamba that is conceptually well-motivated for vascular trajectory modeling. The external validation on a second hospital is a positive feature, as is the reported patient-level cross-validation split. However, the current evidence is weakened by a fundamental inconsistency in the ablation baseline (Table 4 exactly reproduces the VisionFM row of Table 2), which prevents the module contributions from being attributed to the described VAMPIRE architecture, and by the absence of any statistical significance testing for the claimed improvements. These issues are load-bearing for the paper's central claim that the MBD and IEM modules enhance VAMPIRE's CVD prediction.

major comments (3)
  1. [§3.3, Table 4 vs. Table 2] The 'Baseline' row in Table 4 reports F1=0.5522, AUC=0.8061, AUPR=0.6009, which are exactly the values reported for VisionFM in Table 2. Section 2.1 describes VAMPIRE as a Mamba-based architecture following Vim [31], and Vim's Table 2 row is F1=0.4851, AUC=0.7971, AUPR=0.5712. Therefore the ablation baseline is not the backbone used in the described VAMPIRE model; it is a different model (VisionFM). Consequently, the reported improvements from adding MBD and IEM do not measure the modules' contribution to VAMPIRE, and the claim that both modules significantly enhance VAMPIRE's performance is not supported by the printed experiments. The authors must clarify the baseline architecture and re-run the ablation on the actual VAMPIRE backbone (or else explicitly describe and motivate a different composition).
  2. [§2.3 (IEM Module)] The textual descriptions integrated in the IEM module are generated from a classification model trained on OCTA-500 that reads the same input OCTA image; the diagnostic results are then used to prompt GPT-4o for vessel morphology descriptions. This makes the 'morphological knowledge' a function of the input itself, rather than independent prior knowledge, which confounds the interpretation of the IEM module's contribution. The improvements attributed to IEM could stem from the auxiliary classifier's features or from label leakage, not from genuine vessel-shape knowledge. Please provide an analysis that separates these factors—for example, by using a fixed set of text prompts independent of the input, or by directly comparing against feeding the classifier's prediction into the model—to demonstrate that the IEM mechanism itself is responsible for the gains.
  3. [§3.3 (significance claims)] The text states that applying MBD and IEM 'significantly enhance' performance, but no statistical significance tests are reported. For instance, the IEM F1 improvement over baseline in Table 4 is 0.0579, while the standard deviations are 0.0319 and 0.0255, making the effect within one standard deviation. Overlapping standard errors and the absence of paired tests (e.g., across folds, or bootstrap confidence intervals) mean the current claims of significance are not supported. Please add appropriate statistical tests or confidence intervals for the key comparisons in Tables 2 and 4, and temper the language accordingly.
minor comments (5)
  1. [§3.3] The sentence 'applying MBD and IEM independently can still improve the overall result by 6.4%, 2.92% in F1 score and 2.92%, 3.89% in AUPR' is numerically inconsistent with Table 4: the F1 improvements are 0.0640 (MBD) and 0.0579 (IEM), while the 2.92% and 3.89% are the AUPR improvements for MBD and IEM, respectively. Please correct the text to match the table.
  2. [Table 3] The column header 'NTG' appears to be a typo for 'HTG' (high triglycerides).
  3. [§3.2 (Extended Experiment)] The supplementary hospital dataset is described only as '765 images from 379 patients'; no demographic breakdown, acquisition protocol, or image layer details are provided, which limits the interpretability of the external validation in Table 3.
  4. [Introduction] The phrase 'As proved by [28]' is too strong for a single epidemiological association study; consider replacing with 'as shown by' or 'as suggested by'.
  5. [§3.1] There is a typographical double period in 'mean age of 45±12 years..'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: VAMPIRE is a learned image-to-label pipeline whose components are fixed pretrained functions, and no prediction reduces to a fitted parameter or self-citation by construction.

full rationale

VAMPIRE is a learned classifier: OCTA patches are embedded by a Mamba backbone, reordered by vessel-following scans derived from SAM-OCTA segmentation (an external pretrained model), and combined with text embeddings produced by a frozen OCTA-500 disease classifier and GPT-4o; the final CVD labels are learned end-to-end. No equation in Section 2 defines the output in terms of itself, and no parameter is fitted to a subset and then reported as a prediction. The MBD scanning order and IEM text are fixed pretrained functions, not trained on the OCTA-CVD labels. The IEM module does use a classifier that reads the same input image to generate text, so the 'morphological knowledge' is image-derived rather than an independent external ontology; this may weaken a novelty claim, but it does not make the CVD prediction equivalent to an input by construction, because the disease classifier was trained on external OCTA-500 labels and the text encoder is frozen. The Table 4 'Baseline' row numerically matches VisionFM in Table 2, which is an experimental reporting inconsistency that undermines the isolation of MBD/IEM contributions, but it is not a circular derivation: even if the baseline were VisionFM, adding modules and measuring improvement is not equivalent to fitting the labels. The external multi-center comparison (Table 3) provides independent evidence not reducible to in-sample fitting. No load-bearing self-citation or uniqueness theorem is invoked; citations to SAM-OCTA, VisionFM, and Vim are standard external methods. Thus no circular step can be exhibited.

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

The central claim rests mainly on domain assumptions about the informativeness of OCTA vascular structure and the reliability of pretrained segmentation and text generation. No explicit free parameters are introduced beyond standard network and training hyperparameters.

assumptions (4)
  • domain assumption OCTA enface images contain vascular morphology and trajectory features that are predictive of CVD risk and risk factors.
    Stated in the Introduction (paragraph 2) and the basis for the dataset and model design.
  • domain assumption SAM-OCTA segmentation maps provide accurate vessel topology for DFS-based scanning.
    Used in Section 2.2 to construct the vessel-following scanning sequence.
  • domain assumption GPT-4o-generated text descriptions, conditioned on a disease classifier trained on OCTA-500, provide useful morphological priors for CVD risk prediction.
    Used in Section 2.3; the text is derived from the input image, so its informativeness is not independent.
  • standard math Standard Mamba SSM and cross-attention machinery are correct as published.
    Adopted from prior works [7,31] without derivation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction." pith.science (2026). https://pith.science/paper/KNFRTQ3R

@misc{pith2026250720017,
  author       = {Pith},
  title        = {Pith review of: VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KNFRTQ3R}},
  note         = {Machine review of arXiv:2507.20017}
}
read the original abstract

Cardiovascular disease (CVD) remains the leading cause of death worldwide, requiring urgent development of effective risk assessment methods for timely intervention. While current research has introduced non-invasive and efficient approaches to predict CVD risk from retinal imaging with deep learning models, the commonly used fundus photographs and Optical Coherence Tomography (OCT) fail to capture detailed vascular features critical for CVD assessment compared with OCT angiography (OCTA) images. Moreover, existing methods typically classify CVD risk only as high or low, without providing a deeper analysis on CVD-related blood factor conditions, thus limiting prediction accuracy and clinical utility. As a result, we propose a novel multi-purpose paradigm of CVD risk assessment that jointly performs CVD risk and CVD-related condition prediction, aligning with clinical experiences. Based on this core idea, we introduce OCTA-CVD, the first OCTA dataset for CVD risk assessment, and a Vessel-Aware Mamba-based Prediction model with Informative Enhancement (VAMPIRE) based on OCTA enface images. Our proposed model aims to extract crucial vascular characteristics through two key components: (1) a Mamba-Based Directional (MBD) Module that captures fine-grained vascular trajectory features and (2) an Information-Enhanced Morphological (IEM) Module that incorporates comprehensive vessel morphology knowledge. Experimental results demonstrate that our method can surpass standard classification backbones, OCTA-based detection methods, and ophthalmologic foundation models. Our codes and the collected OCTA-CVD dataset are available at https://github.com/xmed-lab/VAMPIRE.

Figures

Figures reproduced from arXiv: 2507.20017 by the authors.

Figure 1
Figure 1. Comparative Analysis of (a) Task Paradigm and (b) Model Capability. (a) Per￾formance Comparison between binary risk classification and joint risk factor prediction. (b) Evaluation of vessel feature representation abilities between OCTA-Based Model and our proposed Vessel- Aware Model. 1 Introduction Cardiovascular diseases (CVDs) remain a significant burden on public health and a leading cause of mortality worldwide… view at source ↗
Figure 2
Figure 2. Overview of the Proposed VAMPIRE Framework, which contains (a) Mamba￾Based Directional (MBD) Module for vascular trajectory feature extraction and (b) Information-Enhanced Morphological Module for vessel shape knowledge integration. I ∈ R B×H×W into flattened patches P ∈ R B×N×h×w, where B represents the batch size, N denotes the number of patches, and (H, W)/(h, w) corresponds to the size of original image/patch. T… view at source ↗
Figure 3
Figure 3. Comparison between Different Scanning Strategies for the Mamba-Based Directional Module [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

31 extracted references · 24 canonical work pages

  1. [31]

    In: Forty-first International Conference on Machine Learning

    Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., Wang, X.: Vision mamba: Efficient visual representation learning with bidirectional state space model. In: Forty-first International Conference on Machine Learning

    work page

  2. [1]

    Cesare, M., Perel, P., Taylor, S., Kabudula, C., Bixby, H., et al.: The heart of the world (2024)

    work page 2024

  3. [2]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Chen, Z., Wang, H., Ou, C., Li, X.: Mutri: Multi-view tri-alignment for oct to octa 3d image translation. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 20885–20894 (2025)

    work page 2025

  4. [3]

    Nature biomedical engineering5(6), 498–508 (2021)

    Cheung, C.Y., Xu, D., Cheng, C.Y., Sabanayagam, C., Tham, Y.C., Yu, M., Rim, T.H., Chai, C.Y., Gopinath, B., Mitchell, P., et al.: A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nature biomedical engineering5(6), 498–508 (2021)

    work page 2021

  5. [4]

    Nature Machine Intelligence4(1), 55–61 (2022)

    Diaz-Pinto, A., Ravikumar, N., Attar, R., Suinesiaputra, A., Zhao, Y., Levelt, E., Dall’Armellina, E., Lorenzi, M., Chen, Q., Keenan, T.D., et al.: Predicting myocardial infarction through retinal scans and minimal personal information. Nature Machine Intelligence4(1), 55–61 (2022)

    work page 2022

  6. [5]

    arXiv preprint arXiv:2010.11929 (2020)

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

    arXiv 2020

  7. [6]

    Nature Reviews Cardiology16(7), 379–381 (2019)

    Farrah, T.E., Webb, D.J., Dhaun, N.: Retinal fingerprints for precision profiling of cardiovascular risk. Nature Reviews Cardiology16(7), 379–381 (2019)

    work page 2019

  8. [7]

    arXiv preprint arXiv:2312.00752 (2023)

    Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)

    arXiv 2023

Show all 31 references
  1. [8]

    npj Digital Medicine7(1), 294 (2024) 10 L

    Hao, J., Kwapong, W.R., Shen, T., Fu, H., Xu, Y., Lu, Q., Liu, S., Zhang, J., Liu, Y., Zhao, Y., et al.: Early detection of dementia through retinal imaging and trustworthy ai. npj Digital Medicine7(1), 294 (2024) 10 L. Wang et al

    2024

  2. [9]

    arXiv preprint arXiv:2407.08083 (2024)

    Hatamizadeh, A., Kautz, J.: Mambavision: A hybrid mamba-transformer vision backbone. arXiv preprint arXiv:2407.08083 (2024)

    2024 arXiv

  3. [10]

    He,K.,Zhang,X.,Ren,S.,Sun,J.:Deepresiduallearningforimagerecognition.In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

    2016

  4. [11]

    npj Digital Medicine6(1), 14 (2023)

    Lee, Y.C., Cha, J., Shim, I., Park, W.Y., Kang, S.W., Lim, D.H., Won, H.H.: Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction. npj Digital Medicine6(1), 14 (2023)

    2023

  5. [12]

    Medical image analysis93, 103092 (2024)

    Li, M., Huang, K., Xu, Q., Yang, J., Zhang, Y., Ji, Z., Xie, K., Yuan, S., Liu, Q., Chen, Q.: Octa-500: a retinal dataset for optical coherence tomography angiogra- phy study. Medical image analysis93, 103092 (2024)

    2024

  6. [13]

    Medical Image Analysis98, 103311 (2024)

    Li, S., Zhang, D., Li, X., Ou, C., An, L., Xu, Y., Yang, W., Zhang, Y., Cheng, K.T.: Vessel-promoted oct to octa image translation by heuristic contextual constraints. Medical Image Analysis98, 103311 (2024)

    2024

  7. [14]

    IEEE Transac- tions on Medical Imaging40(9), 2284–2294 (2021)

    Li, X., Hu, X., Qi, X., Yu, L., Zhao, W., Heng, P.A., Xing, L.: Rotation-oriented collaborative self-supervised learning for retinal disease diagnosis. IEEE Transac- tions on Medical Imaging40(9), 2284–2294 (2021)

    2021

  8. [15]

    IEEE Transactions on Medical Imaging 39(12), 4023–4033 (2020)

    Li, X., Jia, M., Islam, M.T., Yu, L., Xing, L.: Self-supervised feature learning via exploiting multi-modal data for retinal disease diagnosis. IEEE Transactions on Medical Imaging 39(12), 4023–4033 (2020)

    2020

  9. [16]

    arXiv preprint arXiv:2408.05117 (2024)

    Liu, S., Hao, J., Liu, Y., Fu, H., Guo, X., Zhang, S., Zhao, Y.: Beyond the eye: A relational model for early dementia detection using retinal octa images. arXiv preprint arXiv:2408.05117 (2024)

    2024 arXiv

  10. [17]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Liu, S., Hao, J., Xu, Y., Fu, H., Guo, X., Liu, J., Zheng, Y., Liu, Y., Zhang, J., Zhao, Y.: Polar-net: A clinical-friendly model for alzheimer’s disease detection in octa images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 6...

    2023

  11. [18]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Liu,Z.,Hu,H.,Lin,Y.,Yao,Z.,Xie,Z.,Wei,Y.,Ning,J.,Cao,Y.,Zhang,Z.,Dong, L., et al.: Swin transformer v2: Scaling up capacity and resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12009–12019 (2022)

    2022

  12. [19]

    arXiv preprint arXiv:2403.18873 (2024)

    Maldonado-Garcia, C., Bonazzola, R., Ferrante, E., Julian, T.H., Sergouniotis, P.I., Ravikumara,N.,Frangi,A.F.:Predictingriskofcardiovasculardiseaseusingretinal oct imaging. arXiv preprint arXiv:2403.18873 (2024)

    2024 arXiv

  13. [20]

    arXiv preprint arXiv:2410.21276 (2024)

    OpenAI: Gpt-4o system card. arXiv preprint arXiv:2410.21276 (2024)

    2024 arXiv

  14. [21]

    Nature biomedical engineering2(3), 158– 164 (2018)

    Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V., Corrado, G.S., Peng, L., Webster, D.R.: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature biomedical engineering2(3), 158– 164 (2018)

    2018

  15. [22]

    New England Journal of Medicine – Artificial Intelligence1(12), AIoa2300221 (2024)

    Qiu, J., Wu, J., Wei, H., Shi, P., Zhang, M., Sun, Y., Li, L., Liu, H., Liu, H., Hou, S., et al.: Development and validation of a multimodal multitask vision foundation model for generalist ophthalmic artificial intelligence. New England Journal of Medicine – Artificial Intell...

    2024

  16. [23]

    Cir- culation 134(18), 1328–1338 (2016)

    Seidelmann, S.B., Claggett, B., Bravo, P.E., Gupta, A., Farhad, H., Klein, B.E., Klein, R., Di Carli, M., Solomon, S.D.: Retinal vessel calibers in predicting long- term cardiovascular outcomes: the atherosclerosis risk in communities study. Cir- culation 134(18), 1328–1338 (2016)

    2016

  17. [24]

    In: International conference on machine learning

    Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019) VAMPIRE 11

    2019

  18. [25]

    In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

    Wang, C., Chen, X., Ning, H., Li, S.: Sam-octa: A fine-tuning strategy for applying foundation model octa image segmentation tasks. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1771–1775. IEEE (2024)

    2024

  19. [26]

    arXiv preprint arXiv:2409.04356 (2024)

    Wang, H., Chen, Y., Chen, W., Xu, H., Zhao, H., Sheng, B., Fu, H., Yang, G., Zhu, L.: Serp-mamba: Advancing high-resolution retinal vessel segmentation with selective state-space model. arXiv preprint arXiv:2409.04356 (2024)

    2024 arXiv

  20. [27]

    In: International Conference on Med- ical Image Computing and Computer-Assisted Intervention

    Wang, X., Li, H., Xiao, Z., Fu, H., Zhao, Y., Jin, R., Zhang, S., Kwapong, W.R., Zhang, Z., Miao, H., et al.: Screening of dementia on octa images via multi- projection consistency and complementarity. In: International Conference on Med- ical Image Computing and Computer-Assi...

    2022

  21. [28]

    Investigative oph- thalmology & visual science47(6), 2341–2350 (2006)

    Wong, T.Y., Islam, F.A., Klein, R., Klein, B.E., Cotch, M.F., Castro, C., Shar- rett, A.R., Shahar, E.: Retinal vascular caliber, cardiovascular risk factors, and inflammation: the multi-ethnic study of atherosclerosis (mesa). Investigative oph- thalmology & visual science47(6...

    2006

  22. [29]

    Zhao, Y., Hao, J., Kwapong, W., Xu, Y., Shen, T., Fu, H., Lu, Q., Liu, S., Liu, Y., Zhao, Y., et al.: Eye-ad: A graph-based model for early-onset alzheimer’s disease and mild cognitive impairment detection based on retinal octa images (2023)

    2023

  23. [30]

    Nature622(7981), 156–163 (2023)

    Zhou, Y., Chia, M.A., Wagner, S.K., Ayhan, M.S., Williamson, D.J., Struyven, R.R., Liu, T., Xu, M., Lozano, M.G., Woodward-Court, P., et al.: A foundation model for generalizable disease detection from retinal images. Nature622(7981), 156–163 (2023)

    2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.