MedCAGD: Context-Aware Gated Decoder for Robust Medical Image Segmentation

Medical image segmentation relies on the ability of encoder-decoderarchitectures to translate rich feature representations into accurate pixel-level pre-dictions under challenging conditions such as low contrast, structural ambigu-ity, and scale variability. While recent advances in large-scale pretraining andtransformer-based encoders have substantially improved feature extraction, seg-mentation accuracy remains constrained by decoder design, particularly in termsof cross-scale alignment, contextual integration, and boundary preservation. Inthis work, we revisit medical image segmentation from a decoder-centric per-spective and propose a context-aware gated decoder that systematically regulatesfeature fusion and contextual aggregation throughout the decoding process. Theproposed decoder integrates lightweight multi-scale channel recalibration, gatedskip fusion with spatial competition and a global context aggregation mechanismthat injects encoder-wide information into intermediate decoding stages. This de-sign enables effective translation of strong pretrained encoder representations intospatially consistent predictions. Extensive experiments across 11 medical imagesegmentation benchmarks validate the effectiveness and demonstrate that the pro-posed approach consistently outperforms strong baselines while remaining com-putationally practical. Code: https://github.com/saadwazir/MedCAGD