Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation - MICCAI Challenge, FLARE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings

This book constitutes the proceedings of the MICCAI 2024 Challenge, FLARE 2024, held in Conjunction with MICCAI 2024, in Marrakesh, Morocco, during October 2024. 
The 20 full papers included in this book were carefully reviewed and selected from 24 submissions. They describe the solutions the participants found for automatic abdominal organ and pan-cancer segmentation using the official training dataset released for this pupose.
This challenge focuses on both organ and pan-cancer segmentation, including three subtasks:
Subtask 1: Pan-cancer segmentation in CT scans
Subtask 2: Abdominal CT organ segmentation on laptop
Subtask 3: Unsupervised domain adaptation for abdominal organ segmentation in MRI Scans

Summary

This book constitutes the proceedings of the MICCAI 2024 Challenge, FLARE 2024, held in Conjunction with MICCAI 2024, in Marrakesh, Morocco, during October 2024. 
The 20 full papers included in this book were carefully reviewed and selected from 24 submissions. They describe the solutions the participants found for automatic abdominal organ and pan-cancer segmentation using the official training dataset released for this pupose.
This challenge focuses on both organ and pan-cancer segmentation, including three subtasks:
Subtask 1: Pan-cancer segmentation in CT scans
Subtask 2: Abdominal CT organ segmentation on laptop
Subtask 3: Unsupervised domain adaptation for abdominal organ segmentation in MRI Scans

Product details

• Assisted by: Jun Ma (Editor), Wang (Editor), Bo Wang (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 30.08.2025
• EAN: 9783031962011
• ISBN: 978-3-0-3196201-1
• No. of pages: 270
• Dimensions: 155 mm x 16 mm x 235 mm
• Weight: 435 g
• Illustrations: XI, 270 p. 73 illus., 70 illus. in color.
• Series: Lecture Notes in Computer Science
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Application software; Abdomen, Künstliche Intelligenz, Organ, machine learning, Software Engineering, Lehrmittel, Lerntechnologien, E-Learning, Artificial Intelligence, angewandte informatik, Netzwerk-Hardware, Computer Vision, Computer Imaging, Vision, Pattern Recognition and Graphics, Computed Tomography, Computer and Information Systems Applications, Computer Communication Networks, Computers and Education, Computing methodologies, scene understanding, Computer vision tasks, Medical Image Segmentation