Self-Supervised Pre-Training for Digital Dermatology & Data-Centric Approach to Medical Imaging
Creators: name: Gröger, Fabian
Publisher: name: Zenodo
Year: 2023
Subjects: Dermatology
Data-Centric AI
Self-Supervised Learning
Representation Learning
Machine Learning
Deep Learning

description: Training supervised models requires large amounts of labelled data, whose creation is often expensive and time-consuming, especially in the medical domain. The standard practice to mitigate the lack of annotated medical images is to use transfer learning and fine-tune supervised pre-trained ImageNet weights on a downstream task. While this approach achieves satisfactory performance, it still requires a large dataset to adjust the global features for a specific task. Another way to tackle data scarcity in the medical field is by simplifying the data collection process and ordering health workers to document skin conditions. However, this approach usually leads to image collections containing contaminations such as irrelevant samples, duplicates and label errors since this is in stark contrast to prior practice where experts collected samples in standardised conditions.This thesis aims to contribute to the research landscape of deep learning in combination with digital dermatology by producing three scientific articles in the domains of self-supervised learning and dataset cleaning. The first subproject, termed Self-supervised pre-training for dermatological imaging, investigated the possible benefits of using domain-specific self-supervised pre-training for digital dermatology by comparing them against supervised and self-supervised general-purpose pre-training. The second subproject, termed Data-centric approach to medical imaging, introduced a novel semi-automatic data cleaning strategy which can detect various dataset contaminations.The results of the first subproject indicated a clear benefit in using domain-specific self-supervised pre-trained features in terms of reducing the need for annotated samples and increasing linear, kNN and fine-tuning performance for classification and segmentation tasks. Further, a qualitative evaluation revealed that domain-specific pre-training learned more favourable properties for applications in dermatology since it included both local and global features. The results of the second subproject showed that the novel dataset cleaning method is competitive with baseline methods across multiple contamination ratios and datasets. However, the method's performance of label error detection is significantly lower than for the other contamination categories, indicating that there is room for improving the technique. The proposed method was then applied to well-known dermatology benchmarks and found multiple issues in training and test sets, thus destabilising them.
The thesis resulted in three scientific articles almost ready for submission describing the results and insights from the two subprojects. The articles from the first subproject should help advance the paradigm shift from using general-purpose features to using domain-specific self-supervised pre-trained ones. The submission from the second subproject introduced a novel data-cleaning method targeted towards medical imaging to help practitioners in this field produce high-quality image collections.
description: + ID der Publikation: hslu_100438 + Art des Beitrages: Master-/Lizentiats-/Diplomarbeit + Name der Universität / Institution inkl. Ort: Lucerne University of Applied Sciences and Arts, Rotkreuz + Land der Universität / Institution: Switzerland + Sprache: Englisch + Letzte Aktualisierung: 2024-07-30 13:11:58