Keywords: Weakly-supervised learning, Explainability, Tumor detection, Histopathology
TL;DR: We tackle tumor detection via a MIL-like weakly-supervised regression approach to overcome the problem of need for manual annotations or presence of tumor-free slides.
Abstract: In recent years, Multiple Instance Learning (MIL) approaches have gained popularity to address the task of weakly-supervised tumor detection in whole-slide images (WSIs). However, standard MIL relies on classification methods for tumor detection that require negative control, i.e., tumor-free cases, which are challenging to obtain in real-world clinical scenarios, especially when considering surgical resection specimens. Inspired by recent work, in this paper we tackle tumor detection via a MIL-like weakly-supervised regression approach to predict the percentage of tumor present in WSIs, a clinically available target that allows to overcome the problem of need for manual annotations or presence of tumor-free slides. We characterize the quality of such a target by investigating its robustness in the presence of noise on regression percentages and provide explainability through attention maps. We test our approach on breast cancer data from primary tumor and lymph node metastases.