End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction
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by
Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
2021
Abstract
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). Deep attention mechanisms drive
its detection network, targeting salient structures and highly discriminative
feature dimensions across multiple resolutions. Its goal is to accurately
identify csPCa lesions from indolent cancer and the wide range of benign
pathology that can afflict the prostate gland. Simultaneously, a decoupled
residual classifier is used to achieve consistent false positive reduction,
without sacrificing high sensitivity or computational efficiency. In order to
guide model generalization with domain-specific clinical knowledge, a
probabilistic anatomical prior is used to encode the spatial prevalence and
zonal distinction of csPCa. Using a large dataset of 1950 prostate bpMRI paired
with radiologically-estimated annotations, we hypothesize that such CNN-based
models can be trained to detect biopsy-confirmed malignancies in an independent
cohort.
For 486 institutional testing scans, the 3D CAD system achieves
83.69±5.22
false positive(s) per patient, respectively, with 0.882±0.030 AUROC in
patient-based diagnosis -significantly outperforming four state-of-the-art
baseline architectures (U-SEResNet, UNet++, nnU-Net, Attention U-Net) from
recent literature. For 296 external biopsy-confirmed testing scans, the
ensembled CAD system shares moderate agreement with a consensus of expert
radiologists (76.69
(81.08
histologically-confirmed csPCa diagnosis.
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