Prostate Lesion Detection and Salient Feature Assessment Using Zone-Based Classifiers
Published in SSI, 2020
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In this paper, we present an explainable AI pipeline to assess prostate tumor detection from mpMRI scans with Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and PyTorch CNN with saliency mapping for each of three prostate zones.
Abstract: Multi-parametric magnetic resonance imaging (mpMRI) has a growing role in detecting prostate cancer lesions. Thus, it’s pertinent that medical professionals who interpret these scans reduce the risk of human error by using computer-aided detection systems. The variety of algorithms used in system implementation, however, has yielded mixed results. Here we investigate the best machine learning classifier for each prostate zone. We also discover salient features to clarify the models’ classification rationale. Of the data provided, we gathered and augmented T2 weighted images and apparent diffusion coefficient map images to extract first through third order statistical features as input to machine learning classifiers. For our deep learning classifier, we used a convolutional neural net (CNN) architecture for automatic feature extraction and classification. The CNN results’ interpretability was improved by saliency mapping to understand the classification mechanisms within. Ultimately, we concluded that effective detection of peripheral and anterior fibromuscular stroma (AS) lesions depended more on statistical distribution features, whereas those in the transition zone (TZ) depended more on textural features. Ensemble algorithms worked best for PZ and TZ zones, while CNNs were best in the AS zone. These classifiers can be used to validate a radiologist’s predictions and reduce interreader variability in patients suspected to have prostate cancer. The salient features reported in this study can also be investigated further to better understand hidden features and biomarkers of prostate lesions with mpMRIs.