Research Area:  Machine Learning
Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses. Convolutional neural networks have shown strong applicability to various image datasets, enabling detailed features to be learned from the data and, as a result, the ability to classify these images at extremely low error rates. In order to overcome the difficulty in diagnosing breast cancer from mammogram images, we propose our framework for automated breast cancer detection and diagnosis, called BC-DROID, which provides automated region of interest detection and diagnosis using convolutional neural networks. BC-DROID first pretrains based on physician-defined regions of interest in mammogram images. It then trains based on the full mammogram image. The resulting network is able to detect and classify regions of interest as cancerous or benign in one step. We demonstrate the accuracy of our framework-s ability to both locate the regions of interest as well as diagnose them. Our framework achieves a detection accuracy of up to 90% and a classification accuracy of 93.5% (AUC of 92.315%). To the best of our knowledge, this is the first work enabling both automated detection and diagnosis of these areas in one step from full mammogram images. Using our framework-s website, a user can upload a single mammogram image, visualize suspicious regions, and receive the automated diagnoses of these regions.
Author(s) Name:  Richard Platania , Shayan Shams , Seungwon Yang , Jian Zhang , Kisung Lee , Seung-Jong Park
Conferrence name:  ACM-BCB -17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics
Publisher name:  ACM
Volume Information:  Pages 536–543
Paper Link:   https://dl.acm.org/doi/abs/10.1145/3107411.3107484