# Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization

arXiv: https://arxiv.org/abs/2206.06235 · HTML: https://mlsanity.com/paper/2206.06235/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [Very Deep Convolutional Networks for End-to-End Speech Recognition](https://mlsanity.com/paper/1610.03022/) (2016)
- [Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI](https://mlsanity.com/paper/1703.04078/) (2017)

## Built on by (most cited first, 1 of 1)

- [Heterogeneity-Aware Deep Learning for Tumour Classification from Multiparametric MRI](https://mlsanity.com/paper/2608.17254/) (2026)

## Abstract (arXiv)

Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work where we trained a customized convolutional neural network on a public cohort with 201 patients and the cropped 2D patches around the region of interest were used as the input, the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. Something different was peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effectively in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.