Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …

Radiomics is a process that allows the extraction and analysis of quantitative data from
medical images. It is an evolving field of research with many potential applications in …

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …

Recent advances and future perspectives of machine learning techniques offer promising
applications in medical imaging. Machine learning has the potential to improve different …

Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical
courses and outcomes, even within the same tumor stage. This study explores deep …

Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …

Recent advancements in signal processing (SP) and machine learning, coupled with
electronic medical record kee** in hospitals and the availability of extensive sets of …

Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease
detection by using computer-aided diagnosis from fundus image has emerged as a new …

Traditional radiomics involves the extraction of quantitative texture features from medical
images in an attempt to determine correlations with clinical endpoints. We hypothesize that …