Comparison of different classifiers to recognize active bone marrow from CT images

Rosati, Samanta; Franco, Pierfrancesco; Fiandra, C.; Arcadipane, Francesca; Silvetti, P.; Gallio, E.; Panić, Jovana; Ricardi, Umberto; Balestra, Gabriella

doi:10.1109/memea49120.2020.9137173

Cited by 2 publications

(1 citation statement)

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“…Other applications to neurodegenerative disease are: diagnosis of systemic sclerosis based on texture analysis in computed tomography (CT) imaging [124] and diagnosis of multiple sclerosis by comparing different ML algorithms based on 3D T1-MRI and rsfMRI [125], where the approach based on RF feature selection and SVM provided the most accurate result. DL can be used to reconstruct MRI images from the kspace MRI of different sequences [126] or generate MRI which combine information from different modalities [70].…”

Section: Neurological Applicationsmentioning

confidence: 99%

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

Avanzo

Porzio²,

Lorenzon

et al. 2021

Physica Medica

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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 and results. Materials and Methods: A Pubmed search was conducted using terms Artificial Intelligence, Machine Learning, Deep learning, imaging, and Italy as affiliation, excluding reviews and papers outside time interval 2015-2020. In a second phase, participants of the working group AI4MP on Artificial Intelligence of the Italian Association of Physics in Medicine (AIFM) searched for papers on AI in imaging. Results: The Pubmed search produced 794 results. 168 studies were selected, of which 122 were from Pubmed search and 46 from the working group. The most used imaging modality was MRI (44%) followed by CT(12%) ad radiography/mammography (11%). The most common clinical indication were neurological diseases (29%) and diagnosis of cancer (25%). Classification was the most common task for AI (57%) followed by segmentation (16%). 65% of studies used machine learning and 35% used deep learning. We observed a rapid increase of research in Italy on artificial intelligence in the last 5 years, peaking at 155% from 2018 to 2019.

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Section: Neurological Applicationsmentioning

confidence: 99%