Utvidet returrett til 31. januar 2025

Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology

- 5th Miccai Workshop, Grail 2023 and 1st Miccai Challenge, Ocelot 2023, Held in Conjunction with Miccai 2023, Vancouver, Bc, Canada, September 23, and October 4, 2023, Proceedings

Om Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology

This LNCS conference volume constitutes the proceedings of the MICCAI Workshop GRAIL 2023 and MICCAI Challenge OCELOT 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, September 23, and October 4, 2023. The 9 full papers (GRAIL 2023) and 6 full papers (OCELOT 2023) included in this volume were carefully reviewed and selected from GRAIL 14 (GRAIL 2023) and 6 (OCELOT 2023) submissions. The conference GRAIL 2023 a wide set of methods and application and OCELOT 2023 focuses on the cover a wide range of methods utilizing tissue information for better cell detection, in the sense of training strategy, model architecture, and especially how to model cell-tissue relationships.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783031550874
  • Bindende:
  • Paperback
  • Utgitt:
  • 12. mars 2024
  • Dimensjoner:
  • 156x234x10 mm.
  • Vekt:
  • 268 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 13. desember 2024

Beskrivelse av Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology

This LNCS conference volume constitutes the proceedings of the MICCAI Workshop GRAIL 2023 and MICCAI Challenge OCELOT 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, September 23, and October 4, 2023. The 9 full papers (GRAIL 2023) and 6 full papers (OCELOT 2023) included in this volume were carefully reviewed and selected from GRAIL 14 (GRAIL 2023) and 6 (OCELOT 2023) submissions.
The conference GRAIL 2023 a wide set of methods and application and OCELOT 2023 focuses on the cover a wide range of methods utilizing tissue information for better cell detection, in the sense of training strategy, model architecture, and especially how to model cell-tissue relationships.

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