2020/11/30 by Sahithya Ravi, Ravi, Sahithya, Samaneh Khoshrou +3 · 3 citations
Computer Science · Engineering · Medicine · #2019-20 coronavirus outbreak #AI in cancer detection #Artificial intelligence #COVID-19 diagnosis using AI #Cluster analysis #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Coronavirus disease 2019 (COVID-19) #Disease #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Infectious disease (medical specialty) #Internal medicine #Machine Learning (cs.LG) #Medicine #Radiology practices and education #Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) #Virology #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2011.14871
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/11/30 · openalex publication_date 2020/11/30 · arxiv updated 2020/12/01 · openalex created_date 2020/12/07 · openalex updated_date 2026/07/28
In the light of the COVID-19 pandemic, deep learning methods have been widely investigated in detecting COVID-19 from chest X-rays. However, a more pragmatic approach to applying AI methods to a medical diagnosis is designing a framework that facilitates human-machine interaction and expert decision making. Studies have shown that categorization can play an essential rule in accelerating real-world decision making. Inspired by descriptive document clustering, we propose a domain-independent explanatory clustering framework to group contextually related instances and support radiologists' decision making. While most descriptive clustering approaches employ domain-specific characteristics to form meaningful clusters, we focus on model-level explanation as a more general-purpose element of every learning process to achieve cluster homogeneity. We employ DeepSHAP to generate homogeneous clusters in terms of disease severity and describe the clusters using favorable and unfavorable saliency maps, which visualize the class discriminating regions of an image. These human-interpretable maps complement radiologist knowledge to investigate the whole cluster at once. Besides, as part of this study, we evaluate a model based on VGG-19, which can identify COVID and pneumonia cases with a positive predictive value of 95% and 97%, respectively, comparable to the recent explainable approaches for COVID diagnosis.