Multi-Resolution Feature Stem for Diabetic Retinopathy lesion segmentation
Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, requiring automated lesion segmentation using deep learning models for early detection and monitoring. However, DR lesions vary dramatically in size from tiny microaneurysms to large hemorrhages and exudates. This variability creates conflicting demands on the model architecture and input resolution, posing a challenge for effective design. This work investigates the impact of input resolution on different lesion types. Through systematic experimentation with multiple architectures (U-Net, UNet++, Vision Transform
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- PossiblePossibly related (embedding) · 48%pytorch/vision →
- PossiblePossibly related (embedding) · 47%Multi-modal deep learning model for visual acuity prediction from wide field colour fundus imaging - Nature →
- PossiblePossibly related (embedding) · 47%How to improve a 5-class Diabetic Retinopathy model (APTOS 2019) – Mixed predictions across classes[P] →
- PossiblePossibly related (embedding) · 46%DIAGNijmegen/rse-grand-challenge →
- LinkedLinked via arxiv author · 85%Indranil Dutta →
“Multi-Resolution Feature Stem for Diabetic Retinopathy lesion segmentation”
- LinkedLinked via arxiv author · 85%Taehee Jeong →
“Multi-Resolution Feature Stem for Diabetic Retinopathy lesion segmentation”