CalibCause is a quality‑gated, interpretable deep learning framework for diabetic retinopathy grading that integrates visual explanations, label‑free image‑quality conditioning, knowledge distillation, and calibrated confidence with deferral. Using 51,310 fundus images from multiple datasets, the distilled student model achieved a quadratic weighted kappa of 0.863 and accuracy of 0.865 on pooled testing, while maintaining comparable performance across held‑out environments. The framework also demonstrated improved calibration (expected calibration error 0.025–0.036) and higher accuracy (up to 0.923) when deferring the least confident 20% of images.
By Sohaib Sulman, Muhammad Atif Saeed, Akhtar Jamil, Alaa Ali Hameed, Roland Aydin
arXiv:2608. 08294v1 Announce Type: new Abstract: Knowledge distillation trains a smaller student to match the outputs of a larger teacher.
By Abhinand Balachandran, Praveen Prashant
arXiv:2606. 00928v1 Announce Type: cross Abstract: Multiplexed fluorescence microscopy improves tissue segmentation by providing complementary channels including nuclear (DAPI) and membrane (E-cadherin), that together encode richer spatial context than single-channel imaging alone.
By Sakib Mohammad, Jarin Ritu, Md Sakhawat Hossain
The paper presents an explainable diabetic retinopathy classification framework that leverages vision foundation models—DINOv2, CLIP, and Vision Transformer—combined with various transfer learning techniques such as full fine‑tuning, linear probing, and Low‑Rank Adaptation (LoRA). Using the ODIR dataset for internal validation and the APTOS dataset for external testing, DINOv2‑LoRA achieved the best internal AUROC (0.758) while DINOv2 and ViT full fine‑tuning reached the highest external AUROC (0.920). Explainability was assessed with Grad‑CAM and HiResCAM against expert‑annotated lesion masks from IDRiD, using Dice, IoU, and Pointing Game metrics, confirming that model attention aligns with clinically relevant retinal lesions.
By Abhishek Verma, Anila Krishna, Abhishek Gajanan Bankar, Juan Miguel Lopez Alcaraz
arXiv:2605. 31191v2 Announce Type: replace Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on CIFAR-10.
By Umut Onur Yasar
arXiv:2605. 14108v2 Announce Type: replace-cross Abstract: Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness, and automated screening can help extend specialist capacity in resource-constrained clinical workflows.
By Nishi Doshi, Shrey Shah
arXiv:2607. 02569v1 Announce Type: cross Abstract: This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using RETFound, a self-supervised vision-transformer retinal foundation model used here as a frozen feature encoder, and the public APTOS 2019 and DDR diabetic retinopathy fundus image datasets.
By Karim Mardhani
arXiv:2609.05481v1 Announce Type: new
Abstract: Inter example relational distillation transfers a teacher's representation geometry by matching relations among examples within a mini batch. Computing...
By Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi, Omid Kashefi
arXiv:2610.33520v1 Announce Type: new
Abstract: Running deep models for medical image analysis at the point of care is challenging with standard architectures: convolutional neural networks (CNNs) ar...
By Shuaibu Shehu (SR University), Elijah M. Flomo (SR University), Mohammad Safiya (GITAM University), Anurodh Kumar (SR University), Subodh Kumar (Dr. KNS Memorial Institute of Medical Sciences)
arXiv:2609.23561v1 Announce Type: new
Abstract: Deploying efficient neural networks is essential in resource-constrained environments, yet compact models often sacrifice interpretability - a critical...
By Aleks Czufarow, Ihor Babin
The study examines how the choice of camera—Canon versus Nikon—affects image quality and AI performance in retinal screening for diabetic retinopathy. In a Brazilian cohort of 8,524 patients, Nikon images were more than twice as likely to be of inadequate quality compared with Canon, despite being cheaper. Using a frozen pretrained retinal foundation model, the authors evaluated 12 disease classification tasks across different resolutions and classifiers, finding that camera-related performance differences vary widely and are not consistently mitigated by higher resolution or classifier choice.
By Lisa Y. W. Tang
The paper introduces Gated Knowledge‑Guided Prototype Transfer (GProtoT), a training‑free module that enhances zero‑support medical vision‑language models by transferring visual residuals from supported classes to missing ones using VLM similarity and clinical ontology distances. GProtoT employs an evidence gate to scale transfer based on semantic relevance, reducing negative transfer while modestly improving recall and accuracy in realistic one‑shot trials on MESSIDOR. The method requires no gradients, validation data, or extra inference, offering a low‑compute, accountable adaptation path for incomplete clinical supervision.
By Sumaiya Fazal, Rizwan Qureshi, Laleh Behjat, Samira Ebrahimi Kahou