Weakly supervised mitosis detection in breast histopathology images using concentric loss

Abstract

HighlightsAn automatic and accurate system for detecting mitosis in histopathology images.Our method utilizes a deep segmentation network to produce segmentation map. Then a filtering operation is applied to produce the detection results.A novel concentric loss function is proposed to train the semantic segmentation network on weakly supervised mitosis data.We validate our detection system on four widely used mitosis detection datasets and achieve the best performances on three challenging datasets. Graphical abstract Figure. No Caption available. ABSTRACT Developing new deep learning methods for medical image analysis is a prevalent research topic in machine learning. In this paper, we propose a deep learning scheme with a novel loss function for weakly supervised breast cancer diagnosis. According to the Nottingham Grading System, mitotic count plays an important role in breast cancer diagnosis and grading. To determine the cancer grade, pathologists usually need to manually count mitosis from a great deal of histopathology images, which is a very tedious and time‐consuming task. This paper proposes an automatic method for detecting mitosis. We regard the mitosis detection task as a semantic segmentation problem and use a deep fully convolutional network to address it. Different from conventional training data used in semantic segmentation system, the training label of mitosis data is usually in the format of centroid pixel, rather than all the pixels belonging to a mitosis. The centroid label is a kind of weak label, which is much easier to annotate and can save the effort of pathologists a lot. However, technically this weak label is not sufficient for training a mitosis segmentation model. To tackle this problem, we expand the single‐pixel label to a novel label with concentric circles, where the inside circle is a mitotic region and the ring around the inside circle is a “middle ground”. During the training stage, we do not compute the loss of the ring region because it may have the presence of both mitotic and non‐mitotic pixels. This new loss termed as “concentric loss” is able to make the semantic segmentation network be trained with the weakly annotated mitosis data. On the generated segmentation map from the segmentation model, we filter out low confidence and obtain mitotic cells. On the challenging ICPR 2014 MITOSIS dataset and AMIDA13 dataset, we achieve a 0.562 F‐score and 0.673 F‐score respectively, outperforming all previous approaches significantly. On the latest TUPAC16 dataset, we obtain a F‐score of 0.669, which is also the state‐of‐the‐art result. The excellent results quantitatively demonstrate the effectiveness of the proposed mitosis segmentation network with the concentric loss. All of our code has been made publicly available at https://github.com/ChaoLi977/SegMitos_mitosis_detection.