Weakly supervised mitosis detection in breast histopathology images using concentric loss

Medical Image Analysis
Chao LiJunzhou Huang

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 concent...Continue Reading

Citations

Jul 31, 2020·Frontiers of Medicine·Shujian DengYan Xu
Dec 2, 2020·Medical Image Analysis·Ozan Ciga, Anne L Martel
Oct 14, 2020·Medical Image Analysis·Chetan L SrinidhiAnne L Martel
Dec 30, 2020·Diagnostics·Djeane Debora OnthoniPushpanjali Gupta
Apr 13, 2021·Multimedia Tools and Applications·Muralikrishna Puttagunta, S Ravi
Apr 29, 2021·Microscopy Research and Technique·Zhenzhen YouThierry Delzescaux
Apr 21, 2021·Artificial Intelligence in Medicine·Ramin NateghiMohammad Sadegh Helfroush
May 16, 2021·Nature Medicine·Jeroen van der LaakFrancesco Ciompi
Jun 18, 2021·Medical Image Analysis·Anabia SohailAneela Zameer
Jul 1, 2021·Technology in Cancer Research & Treatment·Fahdi KanavatiMasayuki Tsuneki

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