Deep Generative Models [[electronic resource] ] : Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Anirban Mukhopadhyay, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu, Yixuan Yuan

• Autore: Mukhopadhyay Anirban
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (256 pages)
• Disciplina: 006.37
• Soggetto topico: Computer vision; Machine learning; Education - Data processing; Application software; Computer Vision; Machine Learning; Computers and Education; Computer and Information Systems Applications
• Altri autori: OksuzIlkay EngelhardtSandy ZhuDajiang YuanYixuan
• Nota di contenuto: Methods -- Applications -- Methodology. Causal inference. Latent interpretation. Generative factor analysis -- Mammography. Vessel imaging -- Surgical Videos.
• Sommario/riassunto: This LNCS conference volume constitutes the proceedings of the third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 2023. The 23 full papers included in this volume were carefully reviewed and selected from 38 submissions. The conference presents topics ranging from methodology, causal inference, latent interpretation, generative factor analysis to applications such as mammography, vessel imaging, and surgical Videos.
• Titolo autorizzato: Deep Generative Models
• ISBN: 3-031-53767-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910838281103321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14533