Separated collaborative learning for semi-supervised prostate segmentation with multi-site heterogeneous unlabeled MRI data

Zhe Xu, Donghuan Lu, Jie Luo, Yefeng Zheng, Raymond Kai-Yu Tong

Abstract: Highlights•We present a new and practical scenario of multi-site semi-supervised learning (MS-SSL), which allows the enrichment of the unlabeled pool with heterogeneous unlabeled data from multiple arbitrary sites and support the semi-supervised learning in local centers.•We propose a new separated collaborative learning (SCL) framework, including local learning and external multi-site learning, for this under-explored scenario.•We propose a novel local-support category mutual dependence learning scheme, which advocates mutual information-based distribution-insensitive relationship modeling on region-of-interests, for effective collaboration between local labeled data and heterogeneous external unlabeled data to support local learning.•Our method is extensively evaluated on public prostate MRI datasets from six different institutes with varying scanning protocols and patient demographics. The experimental results demonstrate the superiority of our approach.•We also validated the extensibility of our method on the multi-class cardiac MRI segmentation task with data from four different clinical centers.