SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2307.12591v1
• Date: Mon, 24 Jul 2023 08:06:46 GMT
• Title: SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
• Authors: Yiqing Wang, Zihan Li, Jieru Mei, Zihao Wei, Li Liu, Chen Wang, Shengtian Sang, Alan Yuille, Cihang Xie, Yuyin Zhou
• Abstract summary: We present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for medical image analysis. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations. A new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information.
• Score: 32.092182889440814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a notable challenge in acquiring a substantial amount of pre-training data, particularly within the medical field. To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis. Our strategy harnesses the potential of multi-view information by incorporating two principal components. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations through a range of diverse proxy tasks. These tasks span image reconstruction, rotation, contrastive learning, and a novel task that employs a mutual learning paradigm. This new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information from 3D medical data. In the fine-tuning stage, a cross-view decoder is developed to aggregate the multi-view information through a cross-attention block. Compared with the previous state-of-the-art self-supervised learning method Swin UNETR, SwinMM demonstrates a notable advantage on several medical image segmentation tasks. It allows for a smooth integration of multi-view information, significantly boosting both the accuracy and data-efficiency of the model. Code and models are available at https://github.com/UCSC-VLAA/SwinMM/.

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