Affiliations: [a] College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China | [b] Department of Gynaecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China | [c] Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, Madrid, Spain
Correspondence:
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Corresponding author: Tao Song, College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, China. E-mail: t.song@upm.es.
Abstract: Ovarian cancer is a malignant tumor that poses a serious threat to women’s lives. Computer-aided diagnosis (CAD) systems can classify the type of ovarian tumors, but few of them can provide exactly the location information of ovarian cancer cells. Recently, deep learning technology becomes hot for automatic detection of cancer cells, particularly for detecting their locations. In this work, we propose a novel end-to-end network YOLO-OC (Ovarian cancer) model, which can extract the characteristics of ovarian cancer more efficiently. In our method, deformable convolution is used to enhance the model’s ability to learn geometric deformation in space. Squeeze-and-Excitation (SE) module is proposed to automatically learn the importance of different channel features. Data experiments are conducted on datasets collected from The Affiliated Hospital of Qingdao University Medical College, China. Experimental results show that our YOLO-OC model achieves 91.83%, 85.66% and 73.82% on mean average precision mAP@.5, mAP@.75 and mAP@[.5,.95], respectively, which performs better than Faster R-CNN, SSD and RetinaNet on both accuracy and efficiency.
Keywords: Ovarian cancer, deep learning, CT image, object detection, YOLOv3
