SLIViT: a general AI framework for clinical-feature diagnosis from limited 3D biomedical-imaging data

We present SLIViT, a deep-learning framework that accurately measures disease-related risk factors in volumetric biomedical imaging, such as magnetic resonance imaging (MRI) scans, optical coherence tomography (OCT) scans, and ultrasound videos. To evaluate SLIViT, we applied it to five different da...

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Detalles Bibliográficos
Autores principales: Avram*, Oren, Durmus*, Berkin, Rakocz, Nadav, Corradetti, Giulia, An, Ulzee, Nitalla, Muneeswar G., Rudas, Ákos, Wakatsuki, Yu, Hirabayashi, Kazutaka, Velaga, Swetha, Tiosano, Liran, Corvi, Federico, Verma, Aditya, Karamat, Ayesha, Lindenberg, Sophiana, Oncel, Deniz, Almidani, Louay, Hull, Victoria, Fasih-Ahmad, Sohaib, Esmaeilkhanian, Houri, Wykoff, Charles C., Rahmani, Elior, Arnold, Corey W., Zhou, Bolei, Zaitlen, Noah, Gronau, Ilan, Sankararaman, Sriram, Chiang, Jeffrey N., Sadda**, Srinivas R., Halperin**, Eran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690310/
https://www.ncbi.nlm.nih.gov/pubmed/38045283
http://dx.doi.org/10.21203/rs.3.rs-3044914/v2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690310/
https://www.ncbi.nlm.nih.gov/pubmed/38045283
http://dx.doi.org/10.21203/rs.3.rs-3044914/v2

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