Domain Generalization methods in Medical Imaging data analysis
Matsun, Aleksandr
Matsun, Aleksandr
Author
Supervisor
Department
Computer Vision
Embargo End Date
2024-01-01
Type
Thesis
Date
2024
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Language
English
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Abstract
Medical data often exhibits distribution shifts, which cause test-time performance degradation for deep learning models trained using standard supervised learning pipelines, that follow the independent and identically distributed (i.i.d) assumption for train and test data. This challenge is addressed in the field of Domain Generalization (DG) with the sub-field of Single Domain Generalization (SDG) being specifically interesting due to the privacy- or logistics-related issues often associated with medical data. Existing disentanglement-based SDG methods heavily rely on structural information embedded in segmentation masks, however classification labels do not provide such dense information. This work introduces a novel SDG method aimed at medical image classification that leverages channel-wise contrastive disentanglement. It is further enhanced with reconstruction-based style regularization to ensure extraction of distinct style and structure feature representations. We evaluate our method on the complex task of multicenter histopathology image classification, comparing it against state-of-the-art (SOTA) SDG baselines. Results demonstrate that our method surpasses the SOTA by a margin of 1% in average accuracy while also showing more stable performance. This study highlights the importance and challenges of exploring SDG frameworks in the context of the classification task. Aside from that in this work we address the task of Diabetic Retinopathy (DR) grade classification in fundus images in DG setting and introduce an algorithm that re-establishes the model objective function as a maximization of mutual information with a large pretrained model to the medical imaging field. We re-visit the problem of DG in DR classification to establish a clear benchmark with a correct model selection strategy and to achieve robust domain-invariant representation for an improved generalization. Moreover, we conduct extensive experiments on public datasets to show that our proposed method consistently outperforms the previous state-of-the-art by a margin of 5.25% in average accuracy and a lower standard deviation.
Citation
A. Matsun, "Domain Generalization methods in Medical Imaging data analysis", M.S. Thesis, Computer Vision, MBZUAI, Abu Dhabi, UAE, 2024