TY - GEN
T1 - Automatic prostate cancer detection on multi-parametric mri with hierarchical weakly supervised learning
AU - Yang, Haibo
AU - Wu, Guangyu
AU - Shen, Dinggang
AU - Liao, Shu
N1 - Funding Information:
This work was sponsored by Shanghai Pujiang Program (grant No. 19PJ1431900).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - Multi-parametric MRI (mp-MRI) is one of the most commonly used non-invasive methods for prostate cancer (PCa) diagnosis. In recent years, computer aided diagnosis (CAD) for PCa on mp-MRI based on deep learning techniques has gained much attention and shown promising progress. The key for the success of deep learning based PCa diagnosis is to obtain a large amount of high quality PCa region annotation on mp-MRI such that the network can accurately learn the large variation of PCa lesions. In order to precisely annotate the PCa region on mp-MRI, the pathological whole mount data of the patient is normally required as reference, which is often difficult to obtain in real world clinical situations. Therefore, we are motivated to propose a new deep learning based method to integrate different levels of information available in the PCa screening workflow through a multitask hierarchical weakly supervised framework for PCa detection on mp-MRI. Experimental results show that our method achieves promising PCa detection and segmentation results.
AB - Multi-parametric MRI (mp-MRI) is one of the most commonly used non-invasive methods for prostate cancer (PCa) diagnosis. In recent years, computer aided diagnosis (CAD) for PCa on mp-MRI based on deep learning techniques has gained much attention and shown promising progress. The key for the success of deep learning based PCa diagnosis is to obtain a large amount of high quality PCa region annotation on mp-MRI such that the network can accurately learn the large variation of PCa lesions. In order to precisely annotate the PCa region on mp-MRI, the pathological whole mount data of the patient is normally required as reference, which is often difficult to obtain in real world clinical situations. Therefore, we are motivated to propose a new deep learning based method to integrate different levels of information available in the PCa screening workflow through a multitask hierarchical weakly supervised framework for PCa detection on mp-MRI. Experimental results show that our method achieves promising PCa detection and segmentation results.
KW - Deep learning
KW - Multi-parametric MRI
KW - Prostate cancer
KW - Weakly supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85107176780&partnerID=8YFLogxK
U2 - 10.1109/ISBI48211.2021.9434108
DO - 10.1109/ISBI48211.2021.9434108
M3 - Conference contribution
AN - SCOPUS:85107176780
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 316
EP - 319
BT - 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PB - IEEE Computer Society
T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Y2 - 13 April 2021 through 16 April 2021
ER -