From Point Annotations to Epithelial Cell Detection in Breast Cancer Histopathology using RetinaNetDownload PDF

17 Apr 2019 (modified: 05 May 2023)MIDL Abstract 2019Readers: Everyone
Keywords: Epithelial cell detection, RetinaNet, breast histopathology
TL;DR: Investigation of RetinaNet with Adabound optimizer to perform epithelial cell detection and classification from point annotations in whole slide breast histopathology images.
Abstract: Detection of epithelial cells has powerful implications such as being an integral part of nuclear pleomorphism scoring for breast cancer grading. We exploit the point annotations inside nuclei boundaries to estimate their bounding boxes using empirical analysis on the cell bodies and the coarse instance segmentation masks obtained from an image segmentation algorithm. Our experiments show that training a state-of-the-art object detection network with a recently proposed optimizer on simple bounding box estimations performs promising epithelial cell detection, achieving a mean average precision (mAP) score of 71.36% on tumor and 59.65% on benign cells in the test set.
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