Ms. Claire Weissman Profile

Claire Weissman

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Proceedings Article | 4 April 2022 Poster + Paper

Claire Weissman, Lilly Roelofs, Jacob Furst, Daniela Stan Raicu, Roselyne Tchoua

Proceedings Volume 12033, 1203326 (2022) https://doi.org/10.1117/12.2611515

KEYWORDS: Data modeling, Computer aided diagnosis and therapy, Medical imaging, Lung cancer, Solid modeling, Gold, Visualization, Diagnostics, Neural networks, Computer aided design

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Exploring ways to apply deep learning in high-stakes fields like medicine is an emerging research area. In particular, there is a significant amount of research in applying deep learning to medical image classification. The NIH/NCI Lung Image Database Consortium (LIDC) data set allows these techniques to be tested and applied on lung nodule data. It incorporates multiple nodule ratings, including the degree of spiculation, a visual characteristic radiologists consider when diagnosing nodule malignancy. Our ultimate motivation is to improve resource allocation during this process. We aim to flag ambiguous cases that may require more time or more opinions from radiologists. Specifically, using LIDC images, we propose to show a correlation between radiologist semantic disagreement on spiculation ratings and cases with a high level of uncertainty based on our novel methodology, hence flagging “hard” to diagnose cases and assisting radiologists in prioritizing their reviews. Our results show that we can implement meaningful uncertainty scores by clustering image features extracted from a Siamese Convolutional Neural network (SCNN). We found that the nodule images which fell under the highest 33% of our uncertainty scores captured more than 50% of the data with low and no radiologist agreement on spiculation. Moreover, our results flag more images in the spiculated (rather than not spiculated) category, that is images collocated with spiculated images in the feature space, suggesting that we may be capturing important disagreements.

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