MACMD: Multi-dilated Contextual Attention and Channel Mixer Decoding for Medical Image Segmentation

TL;DR

The paper introduces MACMD, a novel decoder for medical image segmentation that combines multi-dilated convolutions, attention mechanisms, and channel mixing to better capture both local details and long-range dependencies, outperforming existing methods.

Contribution

It proposes a MACMD-based decoder that enhances attention and channel mixing, addressing limitations of shallow layer information loss and integration of local and global features.

Findings

Outperforms state-of-the-art methods in Dice score

Improves segmentation accuracy for multiple organs

Enhances computational efficiency

Abstract

Medical image segmentation faces challenges due to variations in anatomical structures. While convolutional neural networks (CNNs) effectively capture local features, they struggle with modeling long-range dependencies. Transformers mitigate this issue with self-attention mechanisms but lack the ability to preserve local contextual information. State-of-the-art models primarily follow an encoder-decoder architecture, achieving notable success. However, two key limitations remain: (1) Shallow layers, which are closer to the input, capture fine-grained details but suffer from information loss as data propagates through deeper layers. (2) Inefficient integration of local details and global context between the encoder and decoder stages. To address these challenges, we propose the MACMD-based decoder, which enhances attention mechanisms and facilitates channel mixing between encoder and decoder stages via skip connections. This design leverages hierarchical dilated convolutions, attention-driven modulation, and a cross channel-mixing module to capture long-range dependencies while preserving local contextual details, essential for precise medical image segmentation. We evaluated our approach using multiple transformer encoders on both binary and multi-organ segmentation tasks. The results demonstrate that our method outperforms state-of-the-art approaches in terms of Dice score and computational efficiency, highlighting its effectiveness in achieving accurate and robust segmentation performance. The code available at https://github.com/lalitmaurya47/MACMD