分割
计算机科学
一致性(知识库)
图像分割
人工智能
注释
训练集
尺度空间分割
正规化(语言学)
编码(集合论)
集合(抽象数据类型)
计算机视觉
机器学习
程序设计语言
作者
Ke Zhang,Xiahai Zhuang
标识
DOI:10.1109/cvpr52688.2022.01136
摘要
Curating a large set of fully annotated training data can be costly, especially for the tasks of medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in practice, but training segmentation models from limited supervision of scribbles is still challenging. To address the difficulties, we propose a new framework for scribble learning-based medical image segmentation, which is composed of mix augmentation and cycle consistency and thus is referred to as CycleMix. For augmentation of supervision, CycleMix adopts the mixup strategy with a dedicated design of random occlusion, to perform increments and decrements of scribbles. For regularization of supervision, CycleMix intensifies the training objective with consistency losses to penalize inconsistent segmentation, which results in significant improvement of segmentation performance. Results on two open datasets, i.e., ACDC and MSCMRseg, showed that the proposed method achieved exhilarating performance, demonstrating comparable or even better accuracy than the fully-supervised methods. The code and expert-made scribble annotationsfor MSCMRseg are publicly available at https://github.com/BWGZK/CycleMix.
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