Shypd.aiThe largest open index of AI toolsSwin-Unet is an open-source research tool that provides the code for a Unet-like pure transformer network for medical image segmentation. It was presented at ECCV 2022 MEDICAL COMPUTER VISION WORKSHOP.
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Swin-Unet is an open-source code repository implementing a Unet-like pure transformer for medical image segmentation, as detailed in the ECCVW 2022 paper "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation." The repository offers updated reproducibility information, including instructions for downloading pre-trained Swin Transformer models and preparing datasets. It outlines the environment setup with Python 3.7 and provides scripts for training and testing the model on datasets like Synapse, BTCV, and ACDC. The project emphasizes the importance of pre-training for pure transformer models, with both encoder and decoder initialized with pre-trained weights to achieve optimal segmentation results.
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