Learning Melanocytic Proliferation Segmentation in Histopathology Images From Imperfect Annotations Publisher



Liu K¹ ; Mokhtari M² ; Li B¹ ; Nofallah S¹ ; May C³ ; Chang O⁴ ; Knezevich S⁵ ; Elmore J⁶ ; Shapiro L² Show Affiliations
Source: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Published:2021

Abstract

Melanoma is the third most common type of skin cancer and is responsible for the most skin cancer deaths. A diagnosis of melanoma is made by the visual interpretation of tissue sections by a pathologist, a challenging task given the complexity and breadth of melanocytic lesions and the subjective nature of biopsy interpretation. We leverage advances in computer vision to aid melanoma diagnosis by segmenting potential regions of lesions on digital images of whole slide skin biopsies. In this study, we demonstrate a Mask-R-CNN-based segmentation framework for such a purpose. To alleviate the cost of data annotation, we leverage a sparse annotation pipeline. Our model can be trained on sparse and noisy labels and achieves state-of-the-art performance in identifying melanocytic proliferations, producing a segmentation with Dice score 0.719, mIOU 0.740 and overall pixel accuracy 0.927. © 2021 IEEE.