TY  - THES
U1  - Master Thesis
A1  - Kovylov, Ivonne
T1  - Prediction of melanoma types using semi-structured Bayesian deep learning models
N2  - 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.
KW  - Deep learning
KW  - Probabilistic modeling
KW  - Interpretability
KW  - Un-certainty
KW  - Statistics
Y2  - 2021
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:kon4-opus4-29743
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:kon4-opus4-29743
SP  - XI, 31 S.
S1  - XI, 31 S.
PB  - Hochschule Konstanz
CY  - Konstanz
ER  -