MaskMed: Decoupled Mask and Class Prediction for Medical Image Segmentation

Bin Xie; Gady Agam

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arXiv · 2511.15603

MaskMed: Decoupled Mask and Class Prediction for Medical Image Segmentation

Bin Xie·Gady Agam

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Abstract

Medical image segmentation typically adopts a point-wise convolutional segmentation head to predict dense labels, where each output channel is heuristically tied to a specific class. This rigid design limits both feature sharing and semantic generalization. In this work, we propose a unified decoupled segmentation head that separates multi-class prediction into class-agnostic mask prediction and class label prediction using shared object queries. Furthermore, we introduce a Full-Scale Aware Deformable Transformer module that enables low-resolution encoder features to attend across full-resolution encoder features via deformable attention, achieving memory-efficient and spatially aligned full-scale fusion. Our proposed method, named MaskMed, achieves state-of-the-art performance, surpassing nnUNet by +2.0% Dice on AMOS 2022 and +6.9% Dice on BTCV.

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Bin Xie, Gady Agam. 2025-11-19. MaskMed: Decoupled Mask and Class Prediction for Medical Image Segmentation. https://arxiv.org/abs/2511.15603

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