Radiomics based likelihood functions for cancer diagnosis

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we...

Discovery Radiomics for Computed Tomography Cancer Detection

Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics ...

Dissimilarity-based representation for radiomics applications

Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information. Many recent studies have proved that radiomics can offer a lot of useful information that physicians cannot extract from the medical images and can be associated with other information like gene or prot...

Prostate cancer Radiomics using multiparametric MR imaging

developing a pattern based on speech acts and language functions for developing materials for the course “ the study of islamic texts translation”

هدف پژوهش حاضر ارائه ی الگویی بر اساس کنش گفتار و کارکرد زبان برای تدوین مطالب درس "بررسی آثار ترجمه شده ی اسلامی" می باشد. در الگوی جدید، جهت تدوین مطالب بهتر و جذاب تر، بر خلاف کتاب-های موجود، از مدل های سطوح گفتارِ آستین (1962)، گروه بندی عملکردهای گفتارِ سرل (1976) و کارکرد زبانیِ هالیدی (1978) بهره جسته شده است. برای این منظور، 57 آیه ی شریفه، به صورت تصادفی از بخش-های مختلف قرآن انتخاب گردید...