Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

扫兴

A Style Transfer-Based Augmentation Framework for Improving Segmentation and Classification Performam the style of a training image into various reference styles, which enriches the information from different sources for the network. FeatAug augments the styles at the feature level to compensate for possible style variations, especially for small-size datasets with limited styles. MaskAug leverage

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的事物

SegNetr: Rethinking the Local-Global Interactions and Skip Connections in U-Shaped Networksial location information of encoder features and achieve accurate fusion with the decoder features. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59% and 76% fewer parameters and GFLOPs than vanilla U-Net, while achieving segmentation performan

撕裂皮肉

rectocele

厨师

LEVER

Frequency-Mixed Single-Source Domain Generalization for Medical Image Segmentation constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves

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