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 …

The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Transformers have recently gained attention in the computer vision domain due to their
ability to model long-range dependencies. However, the self-attention mechanism, which is …

Automated classification of liver lesions in multi-phase CT and MR scans is of clinical
significance but challenging. This study proposes a novel Siamese Dual-Resolution …

Research into the potential benefits of artificial intelligence for comprehending the intricate
biology of cancer has grown as a result of the widespread use of deep learning and …

Medical image segmentation has seen significant improvements with transformer models,
which excel in gras** far-reaching contexts and global contextual information. However …

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range
of downstream tasks have gained significant interest lately in various deep-learning …

Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …

While CNN-based methods have been the cornerstone of medical image segmentation due
to their promising performance and robustness, they suffer from limitations in capturing long …