Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine
learning has increasingly gained relevance because it captures features of disease and …

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

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 …

Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted
minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works …

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 …

Contemporary methods have shown promising results on cardiac image segmentation, but
merely in static learning, ie, optimizing the network once for all, ignoring potential needs for …

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

In clinical settings, where acquisition conditions and patient populations change over time,
continual learning is key for ensuring the safe use of deep neural networks. Yet most …

Deep learning for medical imaging suffers from temporal and privacy-related restrictions on
data availability. To still obtain viable models, continual learning aims to train in sequential …