Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images

Mina Rezaei; Janne J. Näppi; Bernd Bischl; Hiroyuki Yoshida

doi:10.1117/12.2612955

4 April 2022 Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images

Mina Rezaei, Janne J. Näppi, Bernd Bischl, Hiroyuki Yoshida

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203311 (2022) https://doi.org/10.1117/12.2612955
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Deep supervised learning provides an effective approach for developing robust models for various computer-aided diagnosis tasks. However, the underlying assumption is that the frequency of the samples between the different classes of the training dataset is similar or balanced. In real-world medical data, the positive classes often occur too infrequently to satisfy this assumption. Thus, there is an unmet need for deep learning systems that could automatically identify and adapt to the real-world conditions of imbalanced data. In this paper, we propose a novel Bayesian deep ensemble learning framework to address the problem of the representation learning of longtailed and out-of-distribution samples in medical images. By estimating the relative uncertainties of the input data, our framework is able to adapt to the imbalanced data for learning generalizable classifiers. To evaluate the framework, we trained and tested our framework on two public medical imaging datasets that consist of different imbalance ratios and imaging modalities. Our results on the semantic segmentation of high-resolution CT and MRI images achieved 0.93% recall, which represents a 3% relative improvement over previous state-of-the-art ensemble GANs in the handling of the associated long-tailed data and detection of out-of-distribution samples.

Conference Presentation

Citation Download Citation

Mina Rezaei, Janne J. Näppi, Bernd Bischl, and Hiroyuki Yoshida "Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203311 (4 April 2022); https://doi.org/10.1117/12.2612955

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