Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …

Automatic and accurate segmentation of pulmonary nodules in CT images can help
physicians perform more accurate quantitative analysis, diagnose diseases, and improve …

Background: The segment-anything model (SAM), introduced in April 2023, shows promise
as a benchmark model and a universal solution to segment various natural images. It comes …

The application of artificial intelligence (AI) in lung cancer, particularly in surgical
approaches, has significantly transformed the healthcare landscape. AI has demonstrated …

The combination of the U-Net based deep learning models and Transformer is a new trend
for medical image segmentation. U-Net can extract the detailed local semantic and texture …

Lung cancer has been one of the major threats to human life for decades. Computer-aided
diagnosis can help with early lung nodule detection and facilitate subsequent nodule …

Background Accurate grading of semantic characteristics is helpful for radiologists to
determine the probabilities of the likelihood of malignancy of a pulmonary nodule …

摘要医学图像分割是计算机辅助医疗流程中的关键步骤, 精准的医学图像分割可以为诊断与治疗
提供帮助. 分割一切模型(SAM) 利用提示驱动的基础大模型进行下游的分割任务 …

In this work, we propose a processing pipeline for the extraction and identification of
meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon …

Lung cancer is a leading cause of death worldwide and early screening is critical for
improving survival outcomes. In clinical practice, the contextual structure of nodules and the …