arXiv Computer Vision
Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce
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arXiv AI
Sep 28
Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation
The paper investigates combining a domain‑specific self‑supervised task—voxel‑level brain age prediction—with a general task—image inpainting—to pretrain models for brain MRI segmentation. A multitask pretraining framework jointly optimizes both objectives, yielding representations that outperform single‑task pretraining and training from scratch on three segmentation benchmarks (multiple sclerosis lesions, ischemic stroke lesions, and cortical structures). The study demonstrates that integrating domain‑specific and general self‑supervised tasks benefits the development of generalizable neuroimaging foundation models.
arXiv Machine Learning
Jul 8
Towards Interpretable Foundation Models for Retinal Fundus Images
arXiv:2603. 18846v3 Announce Type: replace-cross Abstract: Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL).
arXiv AI
Sep 21
Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
The study proposes a diagnostic-guided approach to choose between stochastic and deterministic longitudinal imaging models based on whether inter-visit changes are driven by disease progression or acquisition variability. Applied to a large, heterogeneous Optos fundus autofluorescence archive, the diagnostic revealed weak time-dependent changes and limited benefit from stochastic models, leading to the development of the deterministic Temporal Retinal U‑Net (TRU). TRU outperformed other classical and deep‑learning comparators on image‑level and eye‑specific progression metrics across held‑out and independent zero‑shot transfer cohorts, though with slightly lower precision in a smaller cross‑vendor cohort.
arXiv Computer Vision
Oct 27
Masked and Predictive Self-Supervised Pretraining for Brain MRI: Effects of Objective Design on Disease Detection
arXiv:2606.13315v2 Announce Type: replace Abstract: MRI self-supervised learning (SSL) has largely emphasized segmentation and dense prediction, while controlled studies of how alternative pretrainin...
arXiv Machine Learning
Oct 7
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
arXiv:2512.23441v2 Announce Type: replace Abstract: Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However, recen...
arXiv AI
Jul 1
Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines
arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.