# Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI

arXiv: https://arxiv.org/abs/1703.04078 · HTML: https://mlsanity.com/paper/1703.04078/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images](https://mlsanity.com/paper/2103.12650/) (2021)
- [Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization](https://mlsanity.com/paper/2206.06235/) (2022)
- [Deep Learning Based Analysis of Prostate Cancer from MP-MRI](https://mlsanity.com/paper/2106.01835/) (2021)
- [Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI](https://mlsanity.com/paper/2107.10806/) (2021)
- [Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis](https://mlsanity.com/paper/2201.09421/) (2022)
- [Prostate Lesion Detection and Salient Feature Assessment Using Zone-Based Classifiers](https://mlsanity.com/paper/2208.11522/) (2022)
- [Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification](https://mlsanity.com/paper/2307.00479/) (2023)

## Abstract (arXiv)

A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved the second highest AUC (0.84) in the PROSTATEx challenge. This study shows the great potential of deep learning for cancer imaging.