← Back to all news
arXiv Computer Vision By Mossa Manisa, Habiba Jahan Priya, A. K. M. Fakhrul Hossain

Knowledge distillation across teacher architectures for compact diabetic retinopathy grading

Read the original on arXiv Computer Vision →

The study evaluates offline response-based knowledge distillation for five-class diabetic retinopathy grading on the APTOS 2019 dataset. Five teacher architectures—ResNet‑50, MobileNetV3‑Large, and Swin Transformer‑Tiny—were distilled into a MobileNetV3‑Small student, exploring a grid of teacher, temperature, and hard‑label weight settings. The best configuration achieved a quadratic weighted kappa of 0.9093 (macro‑F1 0.6851) on a single seed, with an average kappa of 0.9024 across three seeds, while reducing parameters by 18.07× compared to the Swin‑T teacher.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
1d ago

CalibCause: A Quality-Gated, Interpretable and Calibrated Deep Learning Framework for Diabetic Retinopathy Grading

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
efficiencysafety
More like this →
arXiv Machine Learning
Jun 2

Single-Channel Tissue Segmentation via Cross-Modal Distillation from Foundation Models

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
computer-visionefficiencymultimodal
More like this →
arXiv Machine Learning
Aug 31

Explainable Diabetic Retinopathy Classification Using Vision Foundation Models

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
llmscomputer-visionfine-tuning
More like this →