Abstract We introduce Domain-Adaptive Prompt (DAP), a novel method for continual
learning using Vision Transformers (ViT). Prompt-based continual learning has recently …

Automatic segmentation of ground glass opacities and consolidations in chest computer
tomography (CT) scans can potentially ease the burden of radiologists during times of high …

Medical imaging analysis has witnessed remarkable advancements even surpassing
human-level performance in recent years, driven by the rapid development of advanced …

Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning
algorithm should adapt to input data distribution shifts without sacrificing accuracy. These …

As the enthusiasm surrounding Deep Learning grows, both medical practitioners and
regulatory bodies are exploring ways to safely introduce image segmentation in clinical …

Deep learning models have shown a great effectiveness in recognition of findings in medical
images. However, they cannot handle the ever-changing clinical environment, bringing …

The success of deep learning has set new benchmarks for many medical image analysis
tasks. However, deep models often fail to generalize in the presence of distribution shifts …

In real-world clinical settings, traditional deep learning-based classification methods
struggle with diagnosing newly introduced disease types because they require samples …

Continual learning, the ability to acquire knowledge from new data while retaining
previously learned information, is a fundamental challenge in machine learning. Various …

Deep learning models can achieve high accuracy when trained on large amounts of labeled
data. However, real-world scenarios often involve several challenges: Training data may …