Results 71 to 75 of about 5,400 (75)

The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors

Insights into Imaging
Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a ‘black box’ due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical ...
Yixin Wang, Zongtao Hu, Hongzhi Wang
doaj   +1 more source

Intensity Normalization Techniques and Their Effect on the Robustness and Predictive Power of Breast MRI Radiomics [PDF]

arXiv
Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on the acquisition and pre-processing techniques used.
arxiv  

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis [PDF]

arXiv
Current imaging methods for diagnosing BC are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve breast cancer (BC) diagnosis and subtype differentiation.
arxiv  

Machine learning‐based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis

Thoracic Cancer
Background Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity
Yuan Li, Baihan Lyu, Rong Wang, Yue Peng, Haoyu Ran, Bolun Zhou, Yang Liu, Guangyu Bai, Qilin Huai, Xiaowei Chen, Chun Zeng, Qingchen Wu, Cheng Zhang, Shugeng Gao   +13 more
doaj   +1 more source

RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features [PDF]

arXiv
Motivated by the question, "Can we generate tumors with desired attributes?'' this study leverages radiomics features to explore the feasibility of generating synthetic tumor images. Characterized by its low-dimensional yet biologically meaningful markers, radiomics bridges the gap between complex medical imaging data and actionable clinical insights ...
arxiv