Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy
Purpose To perform a systematic review on the research on the application of artificial
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …
How machine learning is powering neuroimaging to improve brain health
This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
Deep learning-based methods for prostate segmentation in magnetic resonance imaging
Featured Application The study demonstrates that high-speed deep learning networks could
perform accurate prostate delineation facilitating the adoption of novel imaging parameters …
perform accurate prostate delineation facilitating the adoption of novel imaging parameters …
Robustness of pet radiomics features: Impact of co-registration with mri
Featured Application The study proposes an analysis of the robustness of Positron Emission
Tomography (PET) radiomics features after PET image co-registration with two different …
Tomography (PET) radiomics features after PET image co-registration with two different …
Customized efficient neural network for COVID-19 infected region identification in CT images
Background: In the field of biomedical imaging, radiomics is a promising approach that aims
to provide quantitative features from images. It is highly dependent on accurate identification …
to provide quantitative features from images. It is highly dependent on accurate identification …
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT …
Y Cui, Z Li, M Xiang, D Han, Y Yin, C Ma - Radiation Oncology, 2022 - Springer
Purpose To construct machine learning models for predicting progression free survival
(PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients …
(PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients …
A systematic review of PET textural analysis and radiomics in cancer
M Piñeiro-Fiel, A Moscoso, V Pubul, Á Ruibal… - Diagnostics, 2021 - mdpi.com
Background: Although many works have supported the utility of PET radiomics, several
authors have raised concerns over the robustness and replicability of the results. This study …
authors have raised concerns over the robustness and replicability of the results. This study …
Hybrid descriptive‐inferential method for key feature selection in prostate cancer radiomics
Abstract In healthcare industry 4.0, a big role is played by radiomics. Radiomics concerns
the extraction and analysis of quantitative information not visible to the naked eye, even by …
the extraction and analysis of quantitative information not visible to the naked eye, even by …
Lung segmentation on high-resolution computerized tomography images using deep learning: a preliminary step for radiomics studies
Background: The aim of this work is to identify an automatic, accurate, and fast deep
learning segmentation approach, applied to the parenchyma, using a very small dataset of …
learning segmentation approach, applied to the parenchyma, using a very small dataset of …
Radiomics: a new biomedical workflow to create a predictive model
Abstract 'Radiomics' is utilized to improve the prediction of patient overall survival and/or
outcome. Target segmentation, feature extraction, feature selection, and classification model …
outcome. Target segmentation, feature extraction, feature selection, and classification model …