transformer论文研读

transformer论文研读

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Conformer: Local Features Coupling Global Representations for Visual Recognition

CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2D/3D medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules - spatial attention and channel attention - are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1×3 and a 3×1 convolutional kernel to capture boundary features. Besides, we extend the 2D attention mechanism to 3D to enhance the network’s ability to aggregate depth information across different layers/slices. The proposed curvilinear structure segmentation network is thoroughly validated using both 2D and 3D images across six different imaging modalities. Experimental results across nine datasets show the proposed method generally outperforms other state-of-the-art algorithms in various metrics.

【多尺度 Attention】Shunted Self-Attention via Multi-Scale Token Aggregation

知乎

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CvT: Introducing Convolutions to Vision Transformers

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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

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CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows

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Focal Self-attention for Local-Global Interactions inVision Transformers

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Unified 2D and 3D Pre-training for Medical Image Classification and Segmentation

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本文提出了通用自监督Transformer (USST) 框架，来打破2D和3D医学图像中预训练之间的障碍，性能更加有效且通用，在六个2D/3D医学图像分类和分割任务上表现良好。

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Improved Transformer for High-Resolution GANs

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