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Artificial Intelligence for Medical Image Analysis with Limited Data

Time: Thu 2024-05-30 13.00

Location: Kollegiesalen, Brinellvägen 8, Stockholm

Language: English

Subject area: Computer Science

Doctoral student: Christos Matsoukas , Beräkningsvetenskap och beräkningsteknik (CST), Kevin Smith

Opponent: Assistant professor Gabriel Eilertsen, Medie- och informationsteknik (MIT) , Linköpings Universitet, Linköping

Supervisor: Associate professor Kevin Smith, Beräkningsvetenskap och beräkningsteknik (CST); Associate professor Josephine Sullivan, Robotik, perception och lärande, RPL; Magnus Söderberg, AstraZeneca

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QC 20240508


Artificial intelligence (AI) is progressively influencing business, science, and society, leading to major socioeconomic changes. However, its application in real-world problems varies significantly across different sectors. One of the primary challenges limiting the widespread adoption of AI in certain areas is data availability. Medical image analysis is one of these domains, where the process of gathering data and labels is often challenging or even infeasible due to legal and privacy concerns, or due to the specific characteristics of diseases. Logistical obstacles, expensive diagnostic methods and the necessity for invasive procedures add to the difficulty of data collection. Even when ample data exists, the substantial cost and logistical hurdles in acquiring expert annotations pose considerable challenges. Thus, there is a pressing need for the development of AI models that can operate in low-data settings.

In this thesis, we explore methods that improve the generalization and robustness of models when data availability is limited. We highlight the importance of model architecture and initialization, considering their associated assumptions and biases, to determine their effectiveness in such settings. We find that models with fewer built-in assumptions in their architecture need to be initialized with pre-trained weights, executed via transfer learning. This prompts us to explore how well transfer learning performs when models are initially trained in the natural domains, where data is abundant, before being used for medical image analysis where data is limited. We identify key factors responsible for transfer learning’s efficacy, and explore its relationship with data size, model architecture, and the distance between the target domain and the one used for pretraining. In cases where expert labels are scarce, we introduce the concept of complementary labels as the means to expand the labeling set. By providing information about other objects in the image, these labels help develop richer representations, leading to improved performance in low-data regimes. We showcase the utility of these methods by streamlining the histopathology-based assessment of chronic kidney disease in an industrial pharmaceutical setting, reducing the turnaround time of study evaluations by 97%. Our results demonstrate that AI models developed for low data regimes are capable of delivering industrial-level performance, proving their practical use in drug discovery and healthcare.