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

2026-07-01

AI Summary

This work addresses limitations in existing medical image segmentation methods, where decoders often struggle with cross-scale alignment, contextual fusion, and boundary preservation, thereby constraining segmentation accuracy. To overcome these challenges, the authors propose a context-aware gated decoder that uniquely integrates gating mechanisms with global context injection. The architecture employs lightweight multi-scale channel recalibration, spatially competitive gated skip connections, and global context aggregation to effectively fuse encoder features and enhance prediction consistency. Evaluated across eleven medical segmentation benchmarks, the proposed method consistently outperforms strong baselines, achieving notable gains in accuracy while maintaining computational efficiency.

Abstract

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

Problem

• medical image segmentation
• decoder design
• cross-scale alignment
• contextual integration
• boundary preservation

Innovation

• context-aware gated decoder
• medical image segmentation
• multi-scale channel recalibration
• gated skip fusion
• global context aggregation