Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation

TL;DR

Med-DisSeg introduces a dispersion-driven framework that enhances representation learning and multi-scale decoding for improved medical image segmentation accuracy across various datasets.

Contribution

It proposes a novel Dispersive Loss and adaptive attention mechanism to address representation collapse and improve fine-grained segmentation in medical images.

Findings

Achieves state-of-the-art performance on five diverse datasets.

Demonstrates robustness and cross-task applicability in multi-organ CT segmentation.

Enhances boundary-aware embeddings with negligible computational overhead.

Abstract

Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, ambiguous boundaries, and large anatomical variability. Similar intensity and texture patterns between targets and surrounding tissues often lead to blurred activations and unreliable separation. We attribute these failures to representation collapse during encoding and insufficient fine grained multi scale decoding. To address these issues, we propose Med DisSeg, a dispersion driven medical image segmentation framework that jointly improves representation learning and anatomical delineation. Med DisSeg combines a lightweight Dispersive Loss with adaptive attention for fine grained structure segmentation. The Dispersive Loss enlarges inter sample margins by treating in batch hidden representations as negative pairs, producing well dispersed and boundary aware embeddings with negligible overhead. Based on these enhanced representations, the encoder strengthens structure sensitive responses, while the decoder performs adaptive multi scale calibration to preserve complementary local texture and global shape information. Extensive experiments on five datasets spanning three imaging modalities demonstrate consistent state of the art performance. Moreover, Med DisSeg achieves competitive results on multi organ CT segmentation, supporting its robustness and cross task applicability.