Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines

Autor: Mattjie, Christian, de Moura, Luis Vinicius, Ravazio, Rafaela Cappelari, Kupssinskü, Lucas Silveira, Parraga, Otávio, Delucis, Marcelo Mussi, Barros, Rodrigo Coelho

Segmentation in medical imaging is a critical component for the diagnosis, monitoring, and treatment of various diseases and medical conditions. Presently, the medical segmentation landscape is dominated by numerous specialized deep learning models,

Externí odkaz: http://arxiv.org/abs/2305.00109

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography.

Autor: de Moura LV; Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil., Mattjie C; Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil.; Graduate Program in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil., Dartora CM; Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil.; Graduate Program in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil., Barros RC; Machine Learning Theory and Applications Lab, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil., Marques da Silva AM; Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil.; Graduate Program in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil.

Publikováno v: Frontiers in digital health [Front Digit Health] 2022 Jan 17; Vol. 3, pp. 662343. Date of Electronic Publication: 2022 Jan 17 (Print Publication: 2021).