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Record Nr. |
UNINA9910746954403321 |
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Autore |
Greenspan Hayit |
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Titolo |
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 [[electronic resource] ] : 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part V / / edited by Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (844 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 14224 |
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Altri autori (Persone) |
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MadabhushiAnant |
MousaviParvin |
SalcudeanSeptimiu |
DuncanJames |
Syeda-MahmoodTanveer |
TaylorRussell |
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Disciplina |
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Soggetti |
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Image processing - Digital techniques |
Computer vision |
Application software |
Machine learning |
Education - Data processing |
Social sciences - Data processing |
Biomedical engineering |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Computer and Information Systems Applications |
Machine Learning |
Computers and Education |
Computer Application in Social and Behavioral Sciences |
Biomedical Engineering and Bioengineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration. |
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