Paper Info
Title
A Deep Learning Approach For Mitosis Detection: Application In Tumor Proliferation Prediction From Whole Slide Images
Abstract
The tumor proliferation, which is correlated with tumor grade, is a crucial biomarker indicative of breast cancer patients? prognosis. The most commonly used method in predicting tumor proliferation speed is the counting of mitotic figures in Hematoxylin and Eosin (H&E) histological slides. Manual mitosis counting is known to suffer from reproducibility problems. This paper presents a fully automated system for tumor proliferation prediction from whole slide images via mitosis counting. First, by considering the epithelial tissue as mitosis activity regions, we build a deep-learning-based region of interest detection method to select the high mitosis activity regions from whole slide images. Second, we learned a set of deep neural networks to detect mitosis detection from selected areas. The proposed mitosis detection system is designed to effectively overcome the mitosis detection challenges by two novel deep preprocessing and two-step hard negative mining approaches. Third, we trained a Support Vector Machine (SVM) classifier to predict the final tumor proliferation score. The proposed method was evaluated on the dataset of the Tumor Proliferation Assessment Challenge (TUPAC16) and achieved a 73.81 % F-measure and 0.612 weighted kappa score, respectively, outperforming all previous approaches significantly. Experimental results demonstrate that the proposed system considerably improves the tumor proliferation prediction accuracy and provides a reliable automated tool to support health care make-decisions.
Year
DOI
Venue
2021
10.1016/j.artmed.2021.102048
ARTIFICIAL INTELLIGENCE IN MEDICINE
Keywords
DocType
Volume
Breast cancer grading, Mitosis detection, Whole-slide image, Deep learning
Journal
114
ISSN
Citations 
PageRank 
0933-3657
1
0.35
References 
Authors
0
3
Name
Order
Citations
PageRank
Ramin Nateghi120.70
Habibollah Danyali24911.07
Mohammad Sadegh Helfroush37011.30