Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
| Autor: | Nichyporuk, Brennan, Cardinell, Jillian, Szeto, Justin, Mehta, Raghav, Falet, Jean-Pierre R., Arnold, Douglas L., Tsaftaris, Sotirios A., Arbel, Tal |
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| Rok vydání: | 2022 |
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| Zdroj: | Machine.Learning.for.Biomedical.Imaging. 1 (2022) |
| Druh dokumentu: | Working Paper |
| Popis: | Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the 'ground-truth' label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified. Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org/papers/2022:029.html |
| Databáze: | arXiv |
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