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

arXiv: https://arxiv.org/abs/2101.03244 · HTML: https://mlsanity.com/paper/2101.03244/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 85 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Adaptive Gradient Methods with Dynamic Bound of Learning Rate](https://mlsanity.com/paper/1902.09843/) (2019)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation](https://mlsanity.com/paper/1810.05162/) (2018)
- [Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation](https://mlsanity.com/paper/1903.03148/) (2019)
- [Revisiting RCNN: On Awakening the Classification Power of Faster RCNN](https://mlsanity.com/paper/1803.06799/) (2018)
- [A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels](https://mlsanity.com/paper/1807.11719/) (2018)
- [USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets](https://mlsanity.com/paper/1904.08254/) (2019)
- [SoftSeg: Advantages of soft versus binary training for image segmentation](https://mlsanity.com/paper/2011.09041/) (2020)
- [DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy](https://mlsanity.com/paper/1702.08192/) (2017)
- [NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation](https://mlsanity.com/paper/1907.11320/) (2019)
- [Prostate Cancer Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1905.13145/) (2019)
- [Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation](https://mlsanity.com/paper/2010.07411/) (2020)
- [CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis](https://mlsanity.com/paper/2008.00119/) (2020)
- [Adversarial Networks for the Detection of Aggressive Prostate Cancer](https://mlsanity.com/paper/1702.08014/) (2017)
- [Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection](https://mlsanity.com/paper/1907.12382/) (2019)
- [Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI](https://mlsanity.com/paper/2011.00263/) (2020)

## Built on by (most cited first, 30 of 32)

- [OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM](https://mlsanity.com/paper/2402.09181/) (2024)
- [SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks](https://mlsanity.com/paper/2311.11969/) (2023)
- [ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans](https://mlsanity.com/paper/2211.13238/) (2022)
- [Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI](https://mlsanity.com/paper/2112.05151/) (2021)
- [Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation](https://mlsanity.com/paper/2307.08279/) (2023)
- [Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography](https://mlsanity.com/paper/2111.15409/) (2021)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection](https://mlsanity.com/paper/2309.03652/) (2023)
- [Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI -Should Different Clinical Objectives Mandate Different Loss Functions?](https://mlsanity.com/paper/2110.12889/) (2021)
- [Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI](https://mlsanity.com/paper/2305.00385/) (2023)
- [Bridging the gap between prostate radiology and pathology through machine learning](https://mlsanity.com/paper/2112.02164/) (2021)
- [Decoupled conditional contrastive learning with variable metadata for prostate lesion detection](https://mlsanity.com/paper/2308.09542/) (2023)
- [SAMRI: Segment Any MRI](https://mlsanity.com/paper/2510.26635/) (2025)
- [Sim4Seg: Boosting Multimodal Multi-disease Medical Diagnosis Segmentation with Region-Aware Vision-Language Similarity Masks](https://mlsanity.com/paper/2511.06665/) (2025)
- [Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images](https://mlsanity.com/paper/2106.02385/) (2021)
- [Z-SSMNet: Zonal-aware Self-supervised Mesh Network for Prostate Cancer Detection and Diagnosis with Bi-parametric MRI](https://mlsanity.com/paper/2212.05808/) (2022)
- [Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection](https://mlsanity.com/paper/2308.05344/) (2023)
- [On undesired emergent behaviors in compound prostate cancer detection systems](https://mlsanity.com/paper/2309.08381/) (2023)
- [Counterfactuals and Uncertainty-Based Explainable Paradigm for the Automated Detection and Segmentation of Renal Cysts in Computed Tomography Images: A Multi-Center Study](https://mlsanity.com/paper/2408.03789/) (2024)
- [Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets](https://mlsanity.com/paper/2408.04777/) (2024)
- [Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound](https://mlsanity.com/paper/2412.10997/) (2024)
- [MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images](https://mlsanity.com/paper/2511.12110/) (2025)
- [Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation](https://mlsanity.com/paper/2512.08216/) (2025)
- [The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data](https://mlsanity.com/paper/2205.07516/) (2022)
- [Prostate Lesion Estimation using Prostate Masks from Biparametric MRI](https://mlsanity.com/paper/2301.09673/) (2023)
- [Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation](https://mlsanity.com/paper/2305.05984/) (2023)
- [Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification](https://mlsanity.com/paper/2307.00479/) (2023)
- [Deep Radiomics Detection of Clinically Significant Prostate Cancer on Multicenter MRI: Initial Comparison to PI-RADS Assessment](https://mlsanity.com/paper/2410.16238/) (2024)
- [Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains](https://mlsanity.com/paper/2411.02466/) (2024)
- [RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection](https://mlsanity.com/paper/2501.02066/) (2025)

## Abstract (arXiv)

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% and 93.19±2.96% detection sensitivity at 0.50 and 1.46 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%; kappa = 0.51±0.04) and independent pathologists (81.08%; kappa = 0.56±0.06); demonstrating strong generalization to histologically-confirmed csPCa diagnosis.