Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification.
@article{arxiv.1903.12331,
title = {A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network},
author = {Weiwei Zong and Joon Lee and Chang Liu and Eric Carver and Aharon Feldman and Branislava Janic and Mohamed Elshaikh and Milan Pantelic and David Hearshen and Indrin Chetty and Benjamin Movsas and Ning Wen},
journal= {arXiv preprint arXiv:1903.12331},
year = {2021}
}