Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model
Doyun Kim, Joowon Chung, Jongmun Choi, Marc D Succi, John Conklin, Maria Gabriela Figueiro Longo, Jeanne B Ackman, Brent P Little, Milena Petranovic, Mannudeep K Kalra, Michael H Lev, Synho Do
Nat Commun . 2022 Apr 6;13(1):1867. doi: 10.1038/s41467-022-29437-8.
The inability to accurately, efficiently label large, open-access medical imaging datasets limits the widespread implementation of artificial intelligence models in healthcare. There have been few attempts, however, to automate the annotation of such public databases; one approach, for example, focused on labor-intensive, manual labeling of subsets of these datasets to be used to train new models. In this study, we describe a method for standardized, automated labeling based on similarity to a previously validated, explainable AI (xAI) model-derived-atlas, for which the user can specify a quantitative threshold for a desired level of accuracy (the probability-of-similarity, pSim metric). We show that our xAI model, by calculating the pSim values for each clinical output label based on comparison to its training-set derived reference atlas, can automatically label the external datasets to a user-selected, high level of accuracy, equaling or exceeding that of human experts. We additionally show that, by fine-tuning the original model using the automatically labelled exams for retraining, performance can be preserved or improved, resulting in a highly accurate, more generalized model.
Read Full Article Here: https://doi.org/10.1038/s41467-022-29437-8