The integration of computer-aided medical image analysis systems into clinical practice is pivotal for tasks such as disease diagnosis, treatment planning, and prognosis prediction. Deep learning-based methods have emerged as a promising approach for medical image analysis, showcasing significant potential. However, the performance of these deep learning models heavily relies on the quality and quantity of the available training data. In practical scenarios, obtaining high-quality medical images can be both expensive and time-consuming, while expert annotation of these images is labor-intensive and prone to errors. Therefore, it is imperative to develop effective and efficient methods for medical image analysis that can achieve satisfactory performance even with limited data and annotat...[ Read more ]

The integration of computer-aided medical image analysis systems into clinical practice is pivotal for tasks such as disease diagnosis, treatment planning, and prognosis prediction. Deep learning-based methods have emerged as a promising approach for medical image analysis, showcasing significant potential. However, the performance of these deep learning models heavily relies on the quality and quantity of the available training data. In practical scenarios, obtaining high-quality medical images can be both expensive and time-consuming, while expert annotation of these images is labor-intensive and prone to errors. Therefore, it is imperative to develop effective and efficient methods for medical image analysis that can achieve satisfactory performance even with limited data and annotations, while also being computationally effective in their development and deployment. In this thesis, our focus centers around three crucial research topics within the realm of effective and efficient medical image analysis: data-efficient learning, label-efficient learning, and model-efficient learning. In data-efficient learning, we delve into techniques that increase the diversity of the dataset through data augmentation, and explore efficient strategies for optimal utilization of the limited available data during the training process. In label-efficient learning, we investigate methods to reduce the annotation cost by leveraging partial labels, and study ways to effectively incorporate these partial labels into the training process to maximize their utility. In model-efficient learning, we undertake the design and examination of lightweight and compact models specifically tailored for medical image analysis tasks. These models aim to strike a balance between computational efficiency and performance. To validate the effectiveness and efficiency of the proposed methods, we conduct extensive experiments across various medical image analysis tasks, including nuclei segmentation, organ segmentation, and dose prediction. Through our research, we aim to contribute to the development of effective and efficient medical image analysis methods, enabling accurate and practical solutions in clinical settings. By addressing the challenges of limited data and annotations, and optimizing the computational aspects of model development, we strive to make significant advancements in the field of medical image analysis.