Impact of hybrid supervision approaches on the performance of artificial intelligence for the classification of chest radiographs.

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ID: 101534
2020
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Abstract
To evaluate the impact of different supervision regimens on the training of artificial intelligence (AI) in the classification of chest radiographs as normal or abnormal in a moderately sized cohort of individuals more likely to be outpatients.In a retrospective study, 7000 consecutive two-view chest radiographs obtained from 2012 to 2015 were labeled as normal or abnormal based on clinical reports. A convolutional neural network (CNN) was trained on this dataset and then evaluated with an unseen subset of 500 radiographs. Five different training approaches were tested: (1) weak supervision and four hybrid approaches combining weak supervision and extra supervision with annotation in (2) an unbalanced set of normal and abnormal cases, (3) a set of only abnormal cases, (4) a set of only normal cases, and (5) a balanced set of normal and abnormal cases. Standard binary classification metrics were assessed.The weakly supervised model achieved an accuracy of 82%, but yielded 75 false negative cases, at a sensitivity of 70.0% and a negative predictive value (NPV) of 75.5%. Extra supervision increased NPV at the expense of the false positive rate and overall accuracy. Extra supervision with training using a balance of abnormal and normal radiographs resulted in the greatest increase in NPV (87.2%), improved sensitivity (92.8%), and reduced the number of false negatives by more than fourfold (18 compared to 75 cases).Extra supervision using a balance of annotated normal and abnormal cases applied to a weakly supervised model can minimize the number of false negative cases when classifying two-view chest radiographs. Further refinement of such hybrid training approaches for AI is warranted to refine models for practical clinical applications.
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ellis2020impactcomputers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ellis, Ryan;Ellestad, Erik;Elicker, Brett;Hope, Michael D;Tosun, Duygu;
Journal Computers in biology and medicine
Year 2020
DOI
S0010-4825(20)30087-1
URL
Keywords
outpatient weak supervision attention mining chest radiographs extra supervision hybrid supervision two-view radiographs

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