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Title: A User Interface to Communicate Interpretable AI Decisions to Radiologists
Tools for computer-aided diagnosis based on deep learning have become increasingly important in the medical field. Such tools can be useful, but require effective communication of their decision-making process in order to safely and meaningfully guide clinical decisions. We present a user interface that incorporates the IAIA-BL model, which interpretably predicts both mass margin and malignancy for breast lesions. The user interface displays the most relevant aspects of the model’s explanation including the predicted margin value, the AI confidence in the prediction, and the two most highly activated prototypes for each case. In addition, this user interface includes full-field and cropped images of the region of interest, as well as a questionnaire suitable for a reader study. Our preliminary results indicate that the model increases the readers’ confidence and accuracy in their decisions on margin and malignancy.  more » « less
Award ID(s):
2222336
PAR ID:
10424535
Author(s) / Creator(s):
; ; ; ; ; ; ;
Editor(s):
Chen, Yan; Mello-Thoms, Claudia R.
Date Published:
Journal Name:
Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment
Volume:
12467
Page Range / eLocation ID:
124670P
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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