ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation

• URL: http://arxiv.org/abs/2309.05674v1
• Date: Sat, 9 Sep 2023 02:18:17 GMT
• Title: ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
• Authors: Xian Lin, Zengqiang Yan, Xianbo Deng, Chuansheng Zheng, and Li Yu
• Abstract summary: We build CNN-style Transformers (ConvFormer) to promote better attention convergence and thus better segmentation performance. In contrast to positional embedding and tokenization, ConvFormer adopts 2D convolution and max-pooling for both position information preservation and feature size reduction.
• Score: 10.727162449071155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers have been extensively studied in medical image segmentation to build pairwise long-range dependence. Yet, relatively limited well-annotated medical image data makes transformers struggle to extract diverse global features, resulting in attention collapse where attention maps become similar or even identical. Comparatively, convolutional neural networks (CNNs) have better convergence properties on small-scale training data but suffer from limited receptive fields. Existing works are dedicated to exploring the combinations of CNN and transformers while ignoring attention collapse, leaving the potential of transformers under-explored. In this paper, we propose to build CNN-style Transformers (ConvFormer) to promote better attention convergence and thus better segmentation performance. Specifically, ConvFormer consists of pooling, CNN-style self-attention (CSA), and convolutional feed-forward network (CFFN) corresponding to tokenization, self-attention, and feed-forward network in vanilla vision transformers. In contrast to positional embedding and tokenization, ConvFormer adopts 2D convolution and max-pooling for both position information preservation and feature size reduction. In this way, CSA takes 2D feature maps as inputs and establishes long-range dependency by constructing self-attention matrices as convolution kernels with adaptive sizes. Following CSA, 2D convolution is utilized for feature refinement through CFFN. Experimental results on multiple datasets demonstrate the effectiveness of ConvFormer working as a plug-and-play module for consistent performance improvement of transformer-based frameworks. Code is available at https://github.com/xianlin7/ConvFormer.

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