A Regional Multiple Instance Learning Network for Whole Slide Image Segmentation

Abstract

Whole slide image (WSI) analysis represents the current gold standard for cancer diagnosis. To date many fully supervised learning methods have been proposed for WSI classification and segmentation. However, these methods are substantially limited by accurate pixel-level labels, which are labor-intensive to obtain. To solve this problem, we developed an end-to-end multiple instance learning (MIL)-based network for WSI segmentation using coarse-grained labels only. Our network consists of two main components. First, we introduce a hybrid transformer architecture, which uses a fusion mechanism to fuse the feature maps of the convolutional neural network (CNN) and transformer. Second, a novel regional MIL aggregator is proposed, which is used to identify the key instances and address the problem of data imbalance. Unlike the current MIL methods that treat each instance as being independent, our method gathers the information from neighborhood pixels of each instance and captures the correlation between instances. We evaluated our network on CAMELYON16. The benchmarking experiments and ablation studies show that the performance of our method is competitive with those of fully supervised methods and is also better than those of previous MIL segmentation methods.