Results 1 to 10 of about 71,655 (241)
Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection [PDF]
arXiv, 2017 While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial.
Shafiee, Mohammad Javad, Wong, Alexander
core +4 more sources
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography [PDF]
, 2016 We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and ...
Bradley, Andrew P. +4 more
core +3 more sources
Dissimilarity-based representation for radiomics applications [PDF]
arXiv, 2018 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.
Bernard, Simon +3 more
core +1 more source
Breast Cancer Research, 2022 Background This study investigated the efficacy of radiomics to predict survival outcome for locally advanced breast cancer (LABC) patients and the association of radiomics with tumor heterogeneity and microenvironment.
Xuanyi Wang +5 more
doaj +2 more sources
Radiomics: the facts and the challenges of image analysis [PDF]
European Radiology Experimental, 2018 Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and ...
Stefania Rizzo +6 more
openalex +2 more sources
The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors. [PDF]
Insights ImagingRadiomics 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 ...
Wang Y, Hu Z, Wang H.
europepmc +2 more sources
ERJ Open Research, 2022 Purpose In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community ...
Akshayaa Vaidyanathan +14 more
doaj +1 more source
Multispectral Differential Reconstruction Strategy for Bioluminescence Tomography
Frontiers in Oncology, 2022 Bioluminescence tomography (BLT) is a promising in vivo molecular imaging tool that allows non-invasive monitoring of physiological and pathological processes at the cellular and molecular levels.
Yanqiu Liu +15 more
doaj +1 more source
Frontiers in Oncology, 2021 Cerenkov luminescence tomography (CLT) is a promising non-invasive optical imaging method with three-dimensional semiquantitative in vivo imaging capability.
Xiao Wei +12 more
doaj +1 more source
Robust imaging habitat computation using voxel-wise radiomics features
Scientific Reports, 2021 Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor ...
Kinga Bernatowicz +5 more
doaj +1 more source