Robust and generalizable AI for medical image processing
Time: Fri 2024-11-08 13.00
Location: Kollegiesalen, Brinellvägen 8, Stockholm
Language: English
Subject area: Computer Science
Doctoral student: Emir Konuk , Beräkningsvetenskap och beräkningsteknik (CST)
Opponent: Associate Professor Fredrik Lindsten, Linköping University, Department of Computer and Information Science, Linköping
Supervisor: Associate Professor Kevin Smith, Beräkningsvetenskap och beräkningsteknik (CST); Professor Christos Dimitrakakis, Chalmers University of Technology, Göteborg
QC 20241017
Abstract
Artificial intelligence (AI) offers significant potential to enhance the accuracy and efficiency of medical diagnosis, monitoring, and treatment. In ovarian cancer, where 70% of cases are detected only at stage III or IV, AI-driven tools could enable earlier detection and improve patient outcomes.However, the safety-critical nature of medicine— where even minor errors can have serious consequences—has led to cautious adoption of AI technologies.To integrate AI into clinical practice, it must not only demonstrate good performance, but also robustness and generalizability across diverse clinical settings.
This thesis investigates the development and evaluation of generalizable and robust AI systems, with a focus on medical image analysis. We begin by addressing key gaps in our understanding of how complexity influences generalization, exploring scaling laws across increasingly complex tasks and analyzing how the performance of foundation models is impacted. Foundation models are becoming vital for AI development in medical imaging, particularly in addressing data scarcity challenges. Adapting these models for medical applications often demands substantial computational resources, particularly due to their large size. To mitigate these computational demands, we propose an efficient method for adapting the robust representations of large foundation models trained on diverse datasets to specific medical tasks, aiming to make foundation models more accessible for medical use without compromising their effectiveness.
Using ovarian cancer as a case study, we develop and rigorously evaluate AI systems for ovarian tumor classification.Our systems demonstrate superior performance compared to both non-expert and expert doctors, with a strong emphasis on ensuring accuracy, generalizability across hospitals, and robustness across diverse patient subgroups.We implement a comprehensive evaluation strategy that tests the AI systems in varied clinical settings, ensuring that they maintain high performance.
Finally, we explore the integration of AI systems into clinical workflows, with a focus on the development of joint human-AI systems. By designing AI systems that collaborate effectively with healthcare professionals, we aim to enhance diagnostic accuracy, reduce doctors' workloads, and optimize the use of healthcare resources. Our collaborative human-AI system is designed to be generalizable across different clinical settings to improve patient care and advance the broader adoption of AI in medical practice, paving the way for more efficient and effective healthcare solutions.