Leveraging Advanced Image Analysis and Learning Using Privileged Information for Clinical Decision Support

Abstract

The field of medical artificial intelligence (AI) has seen significant advancements with the availability of digitalized medical data. Machine learning (ML) and deep learning (DL) models have been developed to leverage these datasets, aiding in clinical decision-making and the delivery of evidence-based care. Medical imaging has particularly benefited from ML and DL algorithms, with successful applications in image classification, segmentation, and detection. Similarly, electronic health records (EHR) data analysis has facilitated risk prediction, disease phenotyping, and treatment outcome assessment. However, the field still faces practical challenges, such as the heterogeneity and missingness of data in EHR, and the scarcity of gold-standard labels in medical imaging.

This thesis aims to address these challenges and contribute to the field of medical AI by developing innovative techniques and methodologies. It focuses on building generalizable and explainable AI models with limited labeled data and leveraging privileged information for clinical decision support. To achieve these objectives, strategies such as bias mitigation, data augmentation, regularization, multi-source data integration, and ensembles are proposed or employed. Furthermore, the thesis investigates the utilization of privileged information, which refers to data or information accessible only during training and not during inference. In the medical field, privileged information is prevalent due to multiple data sources and the varying availability of modalities and variations in medical care protocols. By leveraging privileged information, novel algorithms under the Learning Using Privileged Information (LUPI) paradigm and the Learning Using Partially Available Privileged Information (LUPAPI) paradigm are proposed to enhance model performance and address issues of data missingness in multimodal settings. These algorithms allow models to make predictions without relying on specific data during inference, while still benefiting from its inclusion.

The thesis consists of several chapters that tackle specific tasks and challenges. Chapter 2 presents an automated pipeline for segmenting coronary arteries in X-ray coronary angiography images. Chapter 3 focuses on the diagnosis of acute respiratory distress syndrome (ARDS) using EHR data, while Chapter 4 extends this work by applying the LUPI paradigm and LUPAPI paradigm. Chapter 5 addresses the challenge of label uncertainty in ARDS detection using chest X-ray images. Finally, Chapter 6 concludes the thesis by summarizing the key findings and discussing future directions.

In conclusion, this thesis contributes to the advancement of medical AI by developing techniques for robust and explainable decision-support models with limited labeled data. It also explores the utilization of privileged information to enhance model performance. The proposed methodologies have the potential to improve patient care and outcomes, paving the way for further research and development in the field of medical AI.