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A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features

Authors
 Cameron Severn  ;  Krithika Suresh  ;  Carsten Görg  ;  Yoon Seong Choi  ;  Rajan Jain  ;  Debashis Ghosh 
Citation
 SENSORS, Vol.22(14) : 5205, 2022-07 
Journal Title
SENSORS
Issue Date
2022-07
MeSH
Algorithms ; Glioma* ; Humans ; Machine Learning* ; Magnetic Resonance Imaging / methods ; Radiography
Keywords
explainable machine learning ; information visualization ; medical imaging ; radiomics
Abstract
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as "black boxes". Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.
Files in This Item:
T9992022665.pdf Download
DOI
10.3390/s22145205
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Choi, Yoon Seong(최윤성)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/193436
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