TY - GEN
T1 - Malignancy Detection in Prostate Multi-Parametric MR Images Using U-net with Attention
AU - MacHireddy, Archana
AU - Meermeier, Nicholas
AU - Coakley, Fergus
AU - Song, Xubo
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.
AB - Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.
UR - http://www.scopus.com/inward/record.url?scp=85091048690&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091048690&partnerID=8YFLogxK
U2 - 10.1109/EMBC44109.2020.9176050
DO - 10.1109/EMBC44109.2020.9176050
M3 - Conference contribution
AN - SCOPUS:85091048690
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1520
EP - 1523
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -