Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation [[electronic resource] ] : MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / / edited by Jun Ma, Bo Wang

• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (338 pages)
• Disciplina: 610.285
• Soggetto topico: Image processing—Digital techniques; Computer vision; Artificial intelligence; Computer networks; Application software; Education—Data processing; Software engineering; Computer Imaging, Vision, Pattern Recognition and Graphics; Artificial Intelligence; Computer Communication Networks; Computer and Information Systems Applications; Computers and Education; Software Engineering
• Persona (resp. second.): MaJun; WangBo <1967->
• Note generali: Includes index.
• Sommario/riassunto: This book constitutes the proceedings of the MICCAI 2022 Challenge, FLARE 2022, held in Conjunction with MICCAI 2022, in Singapore, on September 22, 2022. The 28 full papers presented in this book were carefully reviewed and selected from 48 submissions. The papers present research and results for abdominal organ segmentation which has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.
• Titolo autorizzato: Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation
• ISBN: 3-031-23911-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996508670903316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 13816