Automatic prostate cancer detection on multi-parametric mri with hierarchical weakly supervised learning

Haibo Yang, Guangyu Wu, Dinggang Shen, Shu Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages316-319
Number of pages4
ISBN (Electronic)9781665412469
DOIs
Publication statusPublished - 2021 Apr 13
Externally publishedYes
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 2021 Apr 132021 Apr 16

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period21/4/1321/4/16

Keywords

  • Deep learning
  • Multi-parametric MRI
  • Prostate cancer
  • Weakly supervised learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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