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.
@article{arxiv.2206.06235,
title = {Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization},
author = {Weiwei Zong and Eric Carver and Simeng Zhu and Eric Schaff and Daniel Chapman and Joon Lee and Hassan Bagher Ebadian and Indrin Chetty and Benjamin Movsas and Winston Wen and Tarik Alafif and Xiangyun Zong},
journal= {arXiv preprint arXiv:2206.06235},
year = {2022}
}
Comments
arXiv admin note: text overlap with arXiv:1903.12331