Weakly Supervised Segmentation of Hyper-Reflective Foci with Compact Convolutional Transformers and SAM2

• URL: http://arxiv.org/abs/2501.05933v1
• Date: Fri, 10 Jan 2025 12:56:18 GMT
• Title: Weakly Supervised Segmentation of Hyper-Reflective Foci with Compact Convolutional Transformers and SAM2
• Authors: Olivier Morelle, Justus Bisten, Maximilian W. M. Wintergerst, Robert P. Finger, Thomas Schultz,
• Abstract summary: We propose a novel framework that increases spatial resolution of a traditional attention-based Multiple Instance Learning (MIL) approach. We demonstrate that replacing MIL with a Compact Convolutional Transformer (CCT) leads to a substantial increase in segmentation accuracy.
• Score: 0.7340017786387767
• License:
• Abstract: Weakly supervised segmentation has the potential to greatly reduce the annotation effort for training segmentation models for small structures such as hyper-reflective foci (HRF) in optical coherence tomography (OCT). However, most weakly supervised methods either involve a strong downsampling of input images, or only achieve localization at a coarse resolution, both of which are unsatisfactory for small structures. We propose a novel framework that increases the spatial resolution of a traditional attention-based Multiple Instance Learning (MIL) approach by using Layer-wise Relevance Propagation (LRP) to prompt the Segment Anything Model (SAM~2), and increases recall with iterative inference. Moreover, we demonstrate that replacing MIL with a Compact Convolutional Transformer (CCT), which adds a positional encoding, and permits an exchange of information between different regions of the OCT image, leads to a further and substantial increase in segmentation accuracy.

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