PhD thesis defense to be held on November 21, 2023, at 15:00 (Teleteaching Room, NTUA Central Library)

Picture Credit: Ioannis Vezakis

Thesis title: Automated Diagnosis and Prognosis of Pathologies in Medical Imaging: Machine Learning Approaches for Osteosarcoma, Allergic Contact Dermatitis, and Pancreatic Adenocarcinoma

Abstract: In an era characterized by groundbreaking advancements in technology and healthcare, the convergence of machine learning and medicine has emerged as a contemporary frontier poised to reshape the landscape of healthcare. Central to this convergence is the domain of medical image analysis, where the synergy between computational prowess and clinical expertise holds immense promise. Amid escalating demands for precise and effective diagnostic and prognostic tools, this doctoral thesis endeavors to lead the way with novel approaches harnessing the potential of machine learning to diagnose and prognose pathologies across three distinct imaging modalities. Specifically, the research focuses on the analysis of histological images featuring osteosarcoma, multispectral dermoscopy images indicative of contact allergic dermatitis, and preoperative CT scans of patients with pancreatic ductal adenocarcinoma. In the first study, the research delves into the diagnosis of histological osteosarcoma images. A comparative methodological approach is adopted to evaluate modern deep neural networks, trained on a publicly available dataset, for the automated detection of viable and necrotic tumors, as well as healthy tissue. The research emphasizes the importance of careful selection of different network architectures, their depth, and the dimensions of the input images. The second study focuses on the diagnosis of Allergic Contact Dermatitis (ACD) using deep learning models, different pre-processing schemes and image modalities. A deep learning approach is employed, incorporating a pre-processing technique to highlight regions of interest while preserving the overall information of the images. Additionally, the study explores the utility of different image modalities and the combination thereof to achieve optimal diagnostic accuracy. This research represents one of the earliest efforts in automated diagnosis of ACD, paving the way for the development of advanced tools and techniques that expedite diagnosis while reducing clinicians' workload. In the third study, a fully automated approach is proposed for the prognostication of pancreatic adenocarcinoma, utilizing preoperative CT scans of patients undergoing pancreatectomy. This approach entails the use of deep learning techniques for tumor and pancreas segmentation, as well as the extraction of radiomics and clinical features to calculate overall survival using traditional machine learning techniques. The proposed approach is compared with current cancer prognostication techniques, highlighting specific features that exhibit remarkable predictive value.

Supervisor: Professor Emeritus Dimitrios - Dionysios Koutsouris

PhD Student: Ioannis Vezakis