Machine learning methods strive to acquire a robust model during training that can
generalize well to test samples, even under distribution shifts. However, these methods often …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

In medical imaging leveraging continual learning (CL) is key for models to adapt to new
classes and data distributions without forgetting prior knowledge. Existing CL methods often …

Methods for unsupervised domain adaptation (UDA) help to improve the performance of
deep neural networks on unseen domains without any labeled data. Especially in medical …

Deep learning models have achieved remarkable success in medical image classification.
These models are typically trained once on the available annotated images and thus lack …

Generalist segmentation models are increasingly favored for diverse tasks involving various
objects from different image sources. Task-Incremental Learning (TIL) offers a privacy …

Deep Learning has shown great success in reshaping medical imaging, yet it faces
numerous challenges hindering widespread application. Issues like catastrophic forgetting …