Artificial intelligence has significantly advanced computational pathology by enabling high-resolution, clinical-grade tumor segmentation models with state-of-the-art diagnostic accuracy. Creating ...

Pytorch implementation of Final Degree Thesis "Diffusion Model Based Brain Tumor Segmentation Enhanced With Inpainting Method". The code of the repository is adapted from the paper Diffusion Models ...

The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived ...

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes ...

The study adhered to the standards established in the Declaration of Helsinki. The local ethics committees approved the retrospective analysis of imaging data (EK 055/19). All patients provided ...

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans ...

This is an experimental project for Image-Segmentation of Ovarian-Tumor by using Tensorflow-Slightly-Flexible-UNet Model, which is a typical classic Tensorflow2 UNet implementation TensorflowUNet.py ...

Despite great advances in brain tumor segmentation and clear clinical need, translation of state-of-the-art computational methods into clinical routine and scientific practice remains a major ...

Objectives: Liver cancer is among the deadliest cancers and its mortality rate is increasing in the United States [1]. Yttrium-90 (Y-90) radioembolization is being increasingly used for the treatment ...

1 Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 2 Shenzhen College of Advanced Technology, University ...