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Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet
2019
IEEE Transactions on Medical Imaging
Preserved Fulltext
We propose a novel multi-class CNN, FocalNet, to jointly detect PCa lesions and predict their aggressiveness using Gleason score (GS). ...
Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for diagnosing prostate cancer (PCa). ...
We used FocalNet to jointly detect prostate cancer and predict the fine-grained Gleason score groups. ...
doi:10.1109/tmi.2019.2901928
pmid:30835218
fatcat:fidhbaphorfvfe4evabwbc5lny
End-to-end prostate cancer detection in bpMRI via 3D CNNs: effects of attention mechanisms, clinical priori and decoupled false positive reduction
2021
Medical Image Analysis
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We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model2 for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). ...
Its goal is to accurately identify csPCa lesions from indolent cancer and the wide range of benign pathology that can afflict the prostate gland. ...
Acknowledgements The authors would like to acknowledge Maarten de Rooij and Ilse Slootweg from Radboud University Medical Center for the annotation of fully delineated masks of prostate cancer for every ...
doi:10.1016/j.media.2021.102155
pmid:34245943
fatcat:izmd6e4kdbd55ccjsfa7kpn4w4
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction
[article]
2021
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
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We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). ...
Its goal is to accurately identify csPCa lesions from indolent cancer and the wide range of benign pathology that can afflict the prostate gland. ...
Acknowledgements The authors would like to acknowledge Maarten de Rooij and Ilse Slootweg from Radboud University Medical Center for the annotation of fully delineated masks of prostate cancer for every ...
arXiv:2101.03244v10
fatcat:l3nmcrhgsjfspjznv6urmotw7a
ProsRegNet: A Deep Learning Framework for Registration of MRI and Histopathology Images of the Prostate
[article]
2020
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
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This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. ...
This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. ...
Enzmann et al., “Joint prostate cancer detection and
gleason score prediction in mp-mri via focalnet,” IEEE Transactions ...
arXiv:2012.00991v1
fatcat:3dzgjyd2rray7olkx4xjjn3fgy