End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effect of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction

• URL: http://arxiv.org/abs/2101.03244v7
• Date: Tue, 6 Apr 2021 22:30:52 GMT
• Title: End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effect of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction
• Authors: Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
• Abstract summary: We present a novel 3D computer-aided detection and diagnosis (CAD) model for automated localization of clinically significant prostate cancer (csa) in bi-parametric MR imaging (bpMRI) Deep attention mechanisms drive its detection network, targeting multi-resolution, salient structures and highly discriminative feature dimensions. CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent cohort.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a novel 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 multi-resolution, salient structures and highly discriminative feature dimensions, in order to accurately identify csPCa lesions from indolent cancer and the wide range of benign pathology that can afflict the prostate gland. In parallel, a decoupled residual classifier is used to achieve consistent false positive reduction, without sacrificing high sensitivity or computational efficiency. In addition, a probabilistic anatomical prior, which captures the spatial prevalence and zonal distinction of csPCa, is computed and encoded into the CNN architecture to guide model generalization with domain-specific clinical knowledge. We hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent cohort, using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations. For 486 institutional testing scans, the 3D CAD system achieves $83.69\pm5.22\%$ and $93.19\pm2.96\%$ detection sensitivity at $0.50$ and $1.46$ false positive(s) per patient, respectively, and $0.882$ 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 testing scans, the ensembled CAD system shares moderate agreement with a consensus of expert radiologists ($76.69\%$; $kappa=0.51\pm0.04$) and independent pathologists ($81.08\%$; $kappa=0.56\pm0.06$); demonstrating strong generalization to histologically-confirmed csPCa diagnosis.

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