Fast And Low Resource Semi Supervised Abdominal Organ Segmentation


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Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation


Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation

Author: Jun Ma

language: en

Publisher: Springer Nature

Release Date: 2023-01-20


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This book constitutes the proceedings of the MICCAI 2022 Challenge, FLARE 2022, held in Conjunction with MICCAI 2022, in Singapore, on September 22, 2022. The 28 full papers presented in this book were carefully reviewed and selected from 48 submissions. The papers present research and results for abdominal organ segmentation which has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.

Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT


Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT

Author: Jun Ma

language: en

Publisher: Springer Nature

Release Date: 2024-07-01


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This book constitutes the proceedings of the MICCAI 2023 Challenge, FLARE 2023, held in Conjunction with MICCAI 2023, in Vancouver, BC, Canada, on October 8, 2023. The 27 full papers presented in this book were carefully reviewed and selected from 37 submissions. The papers present research and results for abdominal organ segmentation which has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.

Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation


Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation

Author: Jun Ma

language: en

Publisher: Springer Nature

Release Date: 2025-07-09


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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