arXiv Machine Learning July 7, 2026 By Cheng Huang, Jia Zhang, Yi Jiang, Yang Liu, Karanjit Kooner, Yadi Liu, Tsengdar Lee, Yang Xie, Wenqi Shi, Guanghua Xiao

GlaKG: A Biomarker-Centric Fundus Knowledge Graph for Explainable Glaucoma Diagnosis and Risk Assessment

Read the original on arXiv Machine Learning →

arXiv:2607. 04673v1 Announce Type: cross Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer little clinical interpretability.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 7

GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification

arXiv:2508. 03750v2 Announce Type: replace Abstract: Early and accurate glaucoma detection is critical to prevent irreversible vision loss, yet existing AI methods often rely on unimodal inputs and lack interpretability.

By Cheng Huang, Zeyu Han, Weizheng Xie, Karanjit Kooner, Tsengdar Lee, Jui-Kai Wang, Jia Zhang

llms rag multimodal safety

arXiv AI

Jul 31

Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge

arXiv:2603. 07131v4 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis.

By Shuai Lu, Meng Wang, Jia Guo, Jiawei Du, Bo Liu, Shengzhu Yang, Weihang Zhang, Huazhu Fu, Huiqi Li

llms rag nlp benchmarks safety

arXiv AI

Jul 14

From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation

arXiv:2607. 09664v1 Announce Type: new Abstract: To provide a structured and interpretable assessment, we decompose the image-based diagnosis into components following the Toulmin model of argumentation.

By Anca Marginean, Adrian Groza

agents

arXiv Machine Learning

Aug 11

Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing

arXiv:2608. 09752v1 Announce Type: cross Abstract: Retinal fundus images frequently exhibit multiple co-occurring pathologies, yet standard deep learning classifiers apply static, identical computation to every image regardless of the underlying disease distribution.

By Nagur Shareef Shaik, Jeongwoo Park, Yeong-Jin Kim, Jaeuk Jung, Hyunjung Oh, Dong Hye Ye

benchmarks

arXiv Machine Learning

Jul 24

Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification

arXiv:2607. 21068v1 Announce Type: new Abstract: Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized.

By Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya Chatterjee

rag benchmarks safety

arXiv AI

Jun 11

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

arXiv:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.

By Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci, Abeer Badawi, Adibvafa Fallahpour, Arash Afkanpour, Leonid Sigal, Ali Etemad, Elham Dolatabadi

llms fine-tuning multimodal benchmarks safety

Related stories

GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification

Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge

From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation

Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing

Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models