Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Transformer, the model of choice for natural language processing, has drawn scant attention
from the medical imaging community. Given the ability to exploit long-term dependencies …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-
Net face limitations in modeling long-range dependencies. To address this, Transformers …

Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

Medical Visual Question Answering (MedVQA) presents a significant opportunity to enhance
diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret …

Abstract The Cell Tracking Challenge is an ongoing benchmarking initiative that has
become a reference in cell segmentation and tracking algorithm development. Here, we …