Detecting helicobacter pylori in whole slide images via weakly supervised multi-task learning

摘要

Due to the difficulty to accurately define the morphologies of Helicobacter Pylori (H. pylori) and the complexity of dealing with the whole slide images (WSIs), no computer-aided solution has currently been presented for detecting H. pylori infection in WSIs. We present the first image semantic segmentation solution for the computer-aided detection of H. pylori in WSIs. The solution only requires polygon annotations as weak supervision, which roughly, instead of pixel-level accurately, label the H. pylori infected areas in WSIs. We propose a new weakly supervised multi-task learning framework (WSMLF) that aims to improve the segmentation performance by more effectively exploiting the weak supervision. To make more effective usage of the weak supervision, we extract multiple inaccurate targets representing different modes of the true target from the available weak annotations. For improvement of the segmentation performance, we design a weakly supervised multi-task learning algorithm that can automatically learn from the weighted summarization of the extracted multiple inaccurate targets. These two advances constitute the resulting technique WSMLF. Introducing the proposed WSMLF to several common deep image semantic segmentation approaches for the detection of H. pylori in WSIs. we observe that WSMLF can enable these approaches to achieve more reasonable segmentation results, which eventually improve the detection performance of H. pylori by at most 6%. WSMLF provides new thoughts for more effectively employing weak supervision to achieve more effective results for image semantic segmentation.