# Conquering the Retina: Bringing Visual in-Context Learning to OCT

arXiv: https://arxiv.org/abs/2506.15200 · HTML: https://mlsanity.com/paper/2506.15200/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Images Speak in Images: A Generalist Painter for In-Context Visual Learning](https://mlsanity.com/paper/2212.02499/) (2022)
- [Visual Prompting via Image Inpainting](https://mlsanity.com/paper/2209.00647/) (2022)
- [Sequential Modeling Enables Scalable Learning for Large Vision Models](https://mlsanity.com/paper/2312.00785/) (2023)
- [STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training](https://mlsanity.com/paper/2304.06716/) (2023)
- [OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods](https://mlsanity.com/paper/2312.08255/) (2023)
- [Neuralizer: General Neuroimage Analysis without Re-Training](https://mlsanity.com/paper/2305.02644/) (2023)
- [Medical Vision Generalist: Unifying Medical Imaging Tasks in Context](https://mlsanity.com/paper/2406.05565/) (2024)

## Built on by (most cited first, 3 of 3)

- [From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks](https://mlsanity.com/paper/2604.06748/) (2026)
- [Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model](https://mlsanity.com/paper/2606.10905/) (2026)
- [Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks](https://mlsanity.com/paper/2606.10967/) (2026)

## Abstract (arXiv)

Recent advancements in medical image analysis have led to the development of highly specialized models tailored to specific clinical tasks. These models have demonstrated exceptional performance and remain a crucial research direction. Yet, their applicability is limited to predefined tasks, requiring expertise and extensive resources for development and adaptation. In contrast, generalist models offer a different form of utility: allowing medical practitioners to define tasks on the fly without the need for task-specific model development. In this work, we explore how to train generalist models for the domain of retinal optical coherence tomography using visual in-context learning (VICL), i.e., training models to generalize across tasks based on a few examples provided at inference time. To facilitate rigorous assessment, we propose a broad evaluation protocol tailored to VICL in OCT. We extensively evaluate a state-of-the-art medical VICL approach on multiple retinal OCT datasets, establishing a first baseline to highlight the potential and current limitations of in-context learning for OCT. To foster further research and practical adoption, we openly release our code.