Self- And Weakly- Supervised Deep Learning Methods With Applications In Biometric And Biomedical Data

Yavuz, Mehmet Can (2023) Self- And Weakly- Supervised Deep Learning Methods With Applications In Biometric And Biomedical Data. [Thesis]

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This dissertation introduces novel deep learning methodologies for effectively leveraging weakly-labeled biomedical data and uncurated/unlabeled biometric data. The thesis is divided into three major parts. In the first part, we present a classifier that combines 2D and 3D classifiers that are trained with weak supervision using volume-wise labeled CT lung images. The main contribution of the thesis is a new representation learning method, extending the contrastive learning framework with the variational approach. In the second part of the thesis, we present a semisupervised approach using the variational contrastive design, applied to learning face attributes from web-collected face images. This technique, called VCL-PL, is specifically designed to counter the inherent noise found in the collected images. Through various experimental setups, the method demonstrates an enhancement in accuracy over supervised or state-of-the-art self-supervised methods. The last part of the dissertation develops a robust self-supervised learning model, VCL, that combines variational contrastive learning with beta-divergence. This model exhibits better performance than state-of-the-art models when used with unlabeled, uncurated, and noisy datasets. Through the development of these methodological advancements and the introduction of novel datasets, this dissertation contributes to learning from weakly-labeled data in the medical domain and introduces the variational contrastive learning approach that better handles noisy data and low data regimes, in the biometric domain.
Item Type: Thesis
Uncontrolled Keywords: Variational Methods, Weakly-Labeled Data, Self-Supervised, Pseudo-Labeling, Contrastive Learning, Beta-Divergence.-- Varyasyonel Yöntemler, Zayıf Etiketlenmiş Veri, Kendinden Denetimli, Sahte Etiketleme, Karşıt Öğrenme, Beta-Divergence.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Dila Günay
Date Deposited: 22 Dec 2023 12:42
Last Modified: 22 Dec 2023 12:42

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