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Medical Image Computing and Computer Assisted Intervention MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 812, 2023, Proceedings, Part II 1st ed. 2023 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 791 pages, height x width: 235x155 mm, weight: 1252 g, 210 Illustrations, color; 9 Illustrations, black and white; XXXVIII, 791 p. 219 illus., 210 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 14221
  • Izdošanas datums: 02-Oct-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031438949
  • ISBN-13: 9783031438943
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  • Mīkstie vāki
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Medical Image Computing and Computer Assisted Intervention MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 812, 2023, Proceedings, Part II 1st ed. 2023Palielināt
  • Formāts: Paperback / softback, 791 pages, height x width: 235x155 mm, weight: 1252 g, 210 Illustrations, color; 9 Illustrations, black and white; XXXVIII, 791 p. 219 illus., 210 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 14221
  • Izdošanas datums: 02-Oct-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031438949
  • ISBN-13: 9783031438943
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031438943.html
Keywords:
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.
Machine learning learning strategies.- machine learning
explainability, bias, and uncertainty.