Automated prostate cancer grading and diagnosis system using deep learning-based Yolo object detection algorithm
作者：
Highlights：
• Automated prostate cancer detection and diagnosis system was developed.
• The system was developed by applying the Gleason grading on prostate tissue images.
• A new PCa dataset was created by a pathologist and reviewed by two other pathologists.
• The fine-tuned Yolo object detection algorithm was re-trained via the dataset.
• Grading outcomes of the Yolo algorithm was fused with ISUP to make the final decision.
摘要
•Automated prostate cancer detection and diagnosis system was developed.•The system was developed by applying the Gleason grading on prostate tissue images.•A new PCa dataset was created by a pathologist and reviewed by two other pathologists.•The fine-tuned Yolo object detection algorithm was re-trained via the dataset.•Grading outcomes of the Yolo algorithm was fused with ISUP to make the final decision.
论文关键词：Deep learning,Gleason grading,Prostate cancer detection,Prostate tissue classification
论文评审过程：Received 16 June 2021, Revised 7 August 2021, Accepted 29 March 2022, Available online 12 April 2022, Version of Record 22 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117148
