Open international challenges are becoming the de facto standard for assessing computer
vision and image analysis algorithms. In recent years, new methods have extended the …

Medical image interpretation is an essential task for the correct diagnosis of many diseases.
Pathologists, radiologists, physicians, and researchers rely heavily on medical images to …

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and
robotic-assisted interventions. While numerous methods for detecting, segmenting and …

Deep learning (DL) models have provided state-of-the-art performance in various medical
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …

Deep learning-based methods, in particular, convolutional neural networks and fully
convolutional networks are now widely used in the medical image analysis domain. The …

Vision Transformers (ViT) s have recently become popular due to their outstanding modeling
capabilities, in particular for capturing long-range information, and scalability to dataset and …

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Real-world medical image segmentation has tremendous long-tailed complexity of objects,
among which tail conditions correlate with relatively rare diseases and are clinically …

The universal model emerges as a promising trend for medical image segmentation, paving
up the way to build medical imaging large model (MILM). One popular strategy to build …