From Point Annotations to Epithelial Cell Detection in Breast Cancer Histopathology using RetinaNet
C. Mercan, M. Balkenhol, J. van der Laak and F. Ciompi
Medical Imaging with Deep Learning (2019)
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.
A pdf file of this publication is available for personal use. Enter your e-mail address in the box below and press the button. You will receive an e-mail message with a link to the pdf file.
An email message containing a code and instructions to download the following paper has been sent to your email adress.