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Prediction of melanoma types using semi-structured Bayesian deep learning models

  • Interpretability and uncertainty modeling are important key factors for medical applications. Moreover, data in medicine are often available as a combination of unstructured data like images and structured predictors like patient’s metadata. While deep learning models are state-of-the-art for image classification, the models are often referred to as ’black-box’, caused by the lack of interpretability. Moreover, DL models are often yielding point predictions and are too confident about the parameter estimation and outcome predictions. On the other side with statistical regression models, it is possible to obtain interpretable predictor effects and capture parameter and model uncertainty based on the Bayesian approach. In this thesis, a publicly available melanoma dataset, consisting of skin lesions and patient’s age, is used to predict the melanoma types by using a semi-structured model, while interpretable components and model uncertainty is quantified. For Bayesian models, transformation model-based variational inference (TM-VI) method is used to determine the posterior distribution of the parameter. Several model constellations consisting of patient’s age and/or skin lesion were implemented and evaluated. Predictive performance was shown to be best by using a combined model of image and patient’s age, while providing the interpretable posterior distribution of the regression coefficient is possible. In addition, integrating uncertainty in image and tabular parts results in larger variability of the outputs corresponding to high uncertainty of the single model components.

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Metadaten
Author:Ivonne Kovylov
URN:urn:nbn:de:bsz:kon4-opus4-29743
Publisher:Hochschule Konstanz
Place of publication:Konstanz
Advisor:Oliver Dürr, Beate Sick
Document Type:Master's Thesis
Language:English
Year of Publication:2021
Granting Institution:HTWG Konstanz
Date of final exam:2021/12/22
Release Date:2022/01/10
Tag:Deep learning; Probabilistic modeling; Interpretability; Un-certainty; Statistics
Page Number:XI, 31 S.
Institutes:Fakultät Informatik
Institut für Optische Systeme - IOS
Open Access?:Ja
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International