MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation
For medical image segmentation practitioners, this work offers a decoder-centric improvement that enhances segmentation accuracy under challenging conditions without requiring new encoders or large-scale pretraining.
The paper proposes a context-aware gated decoder for medical image segmentation that improves cross-scale alignment, contextual integration, and boundary preservation. The method consistently outperforms strong baselines across 11 benchmarks while remaining computationally practical.
Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging conditions such as low contrast, structural ambiguity, and scale variability. While recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction, segmentation accuracy remains constrained by decoder design, particularly in terms of cross-scale alignment, contextual integration, and boundary preservation. In this work, we revisit medical image segmentation from a decoder-centric perspective and propose a context-aware gated decoder that systematically regulates feature fusion and contextual aggregation throughout the decoding process. The proposed decoder integrates lightweight multi-scale channel recalibration, gated skip fusion with spatial competition and a global context aggregation mechanism that injects encoder-wide information into intermediate decoding stages. This design enables effective translation of strong pretrained encoder representations into spatially consistent predictions. Extensive experiments across 11 medical image segmentation benchmarks validate the effectiveness and demonstrate that the proposed approach consistently outperforms strong baselines while remaining computationally practical. Code: https://github.com/saadwazir/MedCAGD