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FLARE@MICCAI 2022: Singapore
- Jun Ma
, Bo Wang
Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation - MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Lecture Notes in Computer Science 13816, Springer 2022, ISBN 978-3-031-23910-6 - Fan Zhang, Meihuan Wang, Hua Yang:
Self-training with Selective Re-training Improves Abdominal Organ Segmentation in CT Image. 1-10 - YuanKe Pan
Unlabeled Abdominal Multi-organ Image Segmentation Based on Semi-supervised Adversarial Training Strategy. 11-22 - Shoujin Huang
Abdominal CT Organ Segmentation by Accelerated nnUNet with a Coarse to Fine Strategy. 23-34 - Mingze Sun, Yankai Jiang, Heng Guo:
Semi-supervised Detection, Identification and Segmentation for Abdominal Organs. 35-46 - Cancan Chen, Weixin Xu, Rongguo Zhang
An Efficiency Coarse-to-Fine Segmentation Framework for Abdominal Organs Segmentation. 47-55 - Zining Chen, Tianyi Wang, Shihao Han, Yinan Song, Shichao Li:
Semi-supervised Augmented 3D-CNN for FLARE22 Challenge. 56-63 - Haoran Lai
DLUNet: Semi-supervised Learning Based Dual-Light UNet for Multi-organ Segmentation. 64-73 - Hao Chen
Multi-organ Segmentation Based on 2.5D Semi-supervised Learning. 74-86 - Yongzhi Huang
3D Cross-Pseudo Supervision (3D-CPS): A Semi-supervised nnU-Net Architecture for Abdominal Organ Segmentation. 87-100 - Jae Won Choi
Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-supervised Abdominal Organ Segmentation in CT. 101-115 - Natália Alves
Uncertainty-Guided Self-learning Framework for Semi-supervised Multi-organ Segmentation. 116-127 - Gregor Köhler
A Noisy nnU-Net Student for Semi-supervised Abdominal Organ Segmentation. 128-138 - Ilya Kuleshov
CLEF: Contrastive Learning of Equivariant Features in CT Images. 139-151 - Maria G. Baldeon Calisto
Teacher-Student Semi-supervised Approach for Medical Image Segmentation. 152-162 - Minh-Khoi Pham
Semi-supervised Organ Segmentation with Mask Propagation Refinement and Uncertainty Estimation for Data Generation. 163-177 - Ziyan Huang
Revisiting nnU-Net for Iterative Pseudo Labeling and Efficient Sliding Window Inference. 178-189 - Zixiao Zhao
A Simple Mean-Teacher UNet Model for Efficient Abdominal Organ Segmentation. 190-201 - Ershuai Wang
Cascade Dual-decoders Network for Abdominal Organs Segmentation. 202-213 - Chuda Xiao
Semi-supervised 3D U-Net Learning Based on Meta Pseudo Labels. 214-222 - Yi Lv
Coarse to Fine Automatic Segmentation of Abdominal Multiple Organs. 223-232 - Shiman Li
MTSegNet: Semi-supervised Abdominal Organ Segmentation in CT. 233-244 - Hui Meng, Haochen Zhao, Ziniu Yu, Qingfeng Li, Jianwei Niu:
Uncertainty-aware Mean Teacher Framework with Inception and Squeeze-and-Excitation Block for MICCAI FLARE22 Challenge. 245-259 - Jiapeng Zhang
Self-pretrained V-Net Based on PCRL for Abdominal Organ Segmentation. 260-269 - Rui Xin
Abdominal Multi-organ Segmentation Using CNN and Transformer. 270-280 - Wentao Liu, Weijin Xu
Combining Self-training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation. 281-292 - Dengqiang Jia
Semi-supervised Multi-organ Segmentation with Cross Supervision Using Siamese Network. 293-306 - Meng Han, Yijie Qu, Xiangde Luo:
Efficient Semi-supervised Multi-organ Segmentation Using Uncertainty Rectified Pyramid Consistency. 307-317 - Jianwei Gao
A Pseudo-labeling Approach to Semi-supervised Organ Segmentation. 318-326
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