A Deep-Learning Framework for Ovarian Cancer Subtype Classification Using Whole Slide Images.
Authors
Wang C,Yi Q,Aflakian A,Ye J,Arvanitis T,Dearn KD,Hajiyavand A
Affiliations (3)
Affiliations (3)
- Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, UK.
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, UK.
- School of Engineering, University of Birmingham, Edgbaston, Birmingham, UK.
Abstract
Ovarian cancer, a leading cause of cancer-related deaths among women, comprises distinct subtypes each requiring different treatment approaches. This paper presents a deep-learning framework for classifying ovarian cancer subtypes using Whole Slide Imaging (WSI). Our method contains three stages: image tiling, feature extraction, and multi-instance learning. Our approach is trained and validated on a public dataset from 80 distinct patients, achieving up to 89,8% accuracy with a notable improvement in computational efficiency. The results demonstrate the potential of our framework to augment diagnostic precision in clinical settings, offering a scalable solution for the accurate classification of ovarian cancer subtypes.
Topics
Ovarian NeoplasmsDeep LearningImage Interpretation, Computer-AssistedJournal Article