Towards efficient deep learning for medical image analysis

Abstract

The advancement of deep learning in medical image analysis has revolutionized the field of medical diagnostics, improving both the accuracy and efficiency of computer- aided diagnostic systems. Despite substantial progress, significant challenges remain, mainly due to the diversity of medical tasks and the scarcity of high-quality annotated data. This thesis addresses these challenges by proposing efficient deep learning meth- ods that improve the development and evaluation of medical imaging models, ensuring their reliability and effectiveness in clinical settings. First, the thesis presents the Ab- dominal Multi-Organ Segmentation (AMOS) dataset, a robust collection of annotated medical images from different demographics and imaging modalities. AMOS utilizes a semi-automated annotation process powered by a pre-trained model, which not only accelerates annotation, but also improves the accuracy and consistency of the data. This approach helps curate a comprehensive benchmark that reflects the real-world complexity and variability of clinical environments, facilitating rigorous testing and evaluation of medical deep learning applications. Then, to address the diversity of imaging tasks, this thesis presents UXNet, a novel application of Neural Architecture Search (NAS) technology designed to adapt neural network architectures specifically for different medical image analysis tasks. UXNet dynamically adapts to the specifics of the input data and output tasks, optimizing model structures to achieve high levels of accuracy and efficiency in different settings. This reduces the reliance on manual tuning and expert knowledge, streamlining the development process of deep learn- ing solutions for medical imaging. Moreover, recognizing the increasing complexity of deep learning models, the thesis introduces AutoBench, an automated tool for the assessment and governance of these models. Leveraging large language models, Au- toBench automates the creation of evaluation standards and conducts comprehensive performance evaluations, facilitating continuous monitoring and adaptation of model performance in medical applications. Finally, I discuss some future work towards in developing efficient and effective deep learning medical applications.