High-Resolution Micro-Patching: A Zero-Leakage Microaneurysm Baseline

Md. Fahim Habib, Anika Alamgir
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:217-231, 2026.

Abstract

We propose a patient-isolated microaneurysm segmentation scheme that maintains lesion geometry via high-resolution micro-patching. Rather than scaling full fundus images, the technique takes native-resolution spatial crops, preserving small lesion structure that resizing erases. A hybrid encoder-decoder with spatial and channel attention mechanisms suppresses background and emphasises rare vascular abnormalities. Zero-leakage training is applied, and cross-domain performance is evaluated on datasets with varying demographics and resolutions. Results show that native resolution is more robust to distribution shift and consistent in diagnostic sensitivity across unseen domains.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-habib26a, title = {High-Resolution Micro-Patching: A Zero-Leakage Microaneurysm Baseline}, author = {Habib, Md. Fahim and Alamgir, Anika}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {217--231}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/habib26a/habib26a.pdf}, url = {https://proceedings.mlr.press/v319/habib26a.html}, abstract = {We propose a patient-isolated microaneurysm segmentation scheme that maintains lesion geometry via high-resolution micro-patching. Rather than scaling full fundus images, the technique takes native-resolution spatial crops, preserving small lesion structure that resizing erases. A hybrid encoder-decoder with spatial and channel attention mechanisms suppresses background and emphasises rare vascular abnormalities. Zero-leakage training is applied, and cross-domain performance is evaluated on datasets with varying demographics and resolutions. Results show that native resolution is more robust to distribution shift and consistent in diagnostic sensitivity across unseen domains.} }
Endnote
%0 Conference Paper %T High-Resolution Micro-Patching: A Zero-Leakage Microaneurysm Baseline %A Md. Fahim Habib %A Anika Alamgir %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-habib26a %I PMLR %P 217--231 %U https://proceedings.mlr.press/v319/habib26a.html %V 319 %X We propose a patient-isolated microaneurysm segmentation scheme that maintains lesion geometry via high-resolution micro-patching. Rather than scaling full fundus images, the technique takes native-resolution spatial crops, preserving small lesion structure that resizing erases. A hybrid encoder-decoder with spatial and channel attention mechanisms suppresses background and emphasises rare vascular abnormalities. Zero-leakage training is applied, and cross-domain performance is evaluated on datasets with varying demographics and resolutions. Results show that native resolution is more robust to distribution shift and consistent in diagnostic sensitivity across unseen domains.
APA
Habib, M.F. & Alamgir, A.. (2026). High-Resolution Micro-Patching: A Zero-Leakage Microaneurysm Baseline. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:217-231 Available from https://proceedings.mlr.press/v319/habib26a.html.

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