My Account Log in

1 option

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 / edited by Jun Ma, Bo Wang.

Springer Nature - Springer Computer Science eBooks 2026 English International Available online

View online
Format:
Book
Author/Creator:
Ma, Jun.
Contributor:
Wang, Bo.
Series:
Lecture Notes in Computer Science, 1611-3349 ; 15717
Language:
English
Subjects (All):
Image processing--Digital techniques.
Image processing.
Computer vision.
Artificial intelligence.
Computer networks.
Application software.
Education--Data processing.
Education.
Software engineering.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Artificial Intelligence.
Computer Communication Networks.
Computer and Information Systems Applications.
Computers and Education.
Software Engineering.
Local Subjects:
Computer Imaging, Vision, Pattern Recognition and Graphics.
Artificial Intelligence.
Computer Communication Networks.
Computer and Information Systems Applications.
Computers and Education.
Software Engineering.
Physical Description:
1 online resource (476 pages)
Edition:
1st ed. 2025.
Place of Publication:
Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
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.
Notes:
Description based on publisher supplied metadata and other sources.
ISBN:
3-031-96202-8
OCLC:
1528362553

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

Find

Home Release notes

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Find catalog Using Articles+ Using your account