Semi-supervised learning methods for unsupervised domain adaptation in medical imaging segmentation
Machine learning applications make several assumptions regarding the scenario where they are employed. One common assumption is that data distribution in the test environment follows the same distribution of the training set. This assumption is systematically broken in most real-world scenarios; the...
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| Formato: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Recursos: | Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS) |
| Repositorio: | Biblioteca Digital de Teses e Dissertações da PUC_RS |
| Idioma: | inglés |
| OAI Identifier: | oai:tede2.pucrs.br:tede/8843 |
| Acesso em linha: | http://tede2.pucrs.br/tede2/handle/tede/8843 |
| Access Level: | acceso abierto |
| Palavra-chave: | Aprendizado Profundo Adaptação de Domínio Aprendizado Semi-Supervisionado Self-Ensembling Deep Learning Domain Adaptation Semi-Supervised Learning CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
| Resumo: | Machine learning applications make several assumptions regarding the scenario where they are employed. One common assumption is that data distribution in the test environment follows the same distribution of the training set. This assumption is systematically broken in most real-world scenarios; the difference between these distributions is commonly known as domain shift. Unsupervised domain adaptation aims at suppressing this problem by leveraging knowledge with unlabeled data from the test environment. One of the most sensitive fields for domain shift is medical imaging. Due to the heterogeneity in data distributions from scanners, models tend to vary in predictive performance when dealing with images from scanners with no examples in the training set. We propose two contributions in this work. First, we introduce the use of self-ensembling domain adaptation in the field of medical imaging segmentation in a spinal cord grey matter segmentation task. Next, based on the success of self-ensembling, we adapt two other recent work from the semi-supervised learning literature to the same task, namely, unsupervised data augmentation and MixMatch. We conduct ablation studies and other experiments in order to understand the behavior of each method and compare their best results. The results show improvements over training models in a supervised learning fashion and demonstrate that recent semi-supervised learning methods are promising for domain adaptation in medical imaging segmentation. |
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