Reproduce “ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation” on OpenResearch

About this paper

Xian Lin, Zengqiang Yan, Xianbo Deng, Chuansheng Zheng, Li Yu

ConvFormer proposes a plug-and-play module that re-engineers transformer components using CNN-style operations to address attention collapse in medical image segmentation. The method achieved consistent Dice score improvements across diverse datasets and architectures, for example, increasing SETR's performance by 3.86% on the ACDC dataset, and demonstrated superior stability over other attention collapse mitigation techniques.

Key insights

• Re-imagining transformer operations in a CNN-style promotes better attention convergence and effectively mitigates attention collapse in data-limited medical imaging environments.
• Dynamically constructing kernel-scalable convolutions allows for an adaptive balance between local feature extraction and global long-range dependency modeling, crucial for varied structures in medical images.
• Integrating convolutional components throughout the transformer architecture helps eliminate the "training tension" observed in some hybrid CNN-Transformer models, leading to more stable and efficient learning.

Summary by alphaXiv.