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ORCIDFLARE@MICCAI 2023: Vancouver, BC, Canada
- Jun Ma, Bo Wang:
Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT - MICCAI Challenge, FLARE 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. Lecture Notes in Computer Science 14544, Springer 2024, ISBN 978-3-031-58775-7 - Yajun Wu
, Ershuai Wang, Zhenzhou Shao:
Fast Abdomen Organ and Tumor Segmentation with nn-UNet. 1-14 - Ziyan Huang, Jin Ye, Haoyu Wang, Zhongying Deng, Tianbin Li, Junjun He:
Exploiting Pseudo-labeling and nnU-Netv2 Inference Acceleration for Abdominal Multi-organ and Pan-Cancer Segmentation. 15-27 - Qin Zhou, Peng Liu, Guoyan Zheng:
Context-Aware Cutmix is All You Need for Universal Organ and Cancer Segmentation. 28-40 - Xinye Yang, Xuru Zhang, Xiaochao Yan, Wangbin Ding, Hao Chen, Liqin Huang:
Abdomen Multi-organ Segmentation Using Pseudo Labels and Two-Stage. 41-53 - Jianwei Gao, Juan Xu, Honggao Fei, Dazhu Liang:
A Two-Step Deep Learning Approach for Abdominal Organ Segmentation. 54-62 - Li Mao:
Semi-supervised Two-Stage Abdominal Organ and Tumor Segmentation Model with Pseudo-labeling. 63-75 - Ruixiang Lei, Mingjing Yang:
2.5D U-Net for Abdominal Multi-organ Segmentation. 76-83 - Pengju Lyu, Junchen Xiong, Wei Fang, Weifeng Zhang, Cheng Wang, Jianjun Zhu:
Advancing Multi-organ and Pan-Cancer Segmentation in Abdominal CT Scans Through Scale-Aware and Self-attentive Modulation. 84-101 - Shuo Wang, Yanjun Peng:
Combine Synergetic Approach with Multi-scale Feature Fusion for Boosting Abdominal Multi-organ and Pan-Cancer Segmentation. 102-114 - Hui Meng, Haochen Zhao, Deqian Yang, Songping Wang, Zhenpeng Li:
Coarse to Fine Segmentation Method Enables Accurate and Efficient Segmentation of Organs and Tumor in Abdominal CT. 115-129 - He Li, Meng Han, Guotai Wang:
Abdominal Organs and Pan-Cancer Segmentation Based on Self-supervised Pre-training and Self-training. 130-142 - Wentao Liu, Tong Tian, Weijin Xu, Lemeng Wang, Haoyuan Li, Huihua Yang:
Two-Stage Hybrid Supervision Framework for Fast, Low-Resource, and Accurate Organ and Pan-Cancer Segmentation in Abdomen CT. 143-154 - Tao Wang, Xiaoling Zhang, Wei Xiong, Shuoling Zhou, Xinyue Zhang:
Semi-Supervised Learning Based Cascaded Pocket U-Net for Organ and Pan-Cancer Segmentation in Abdomen CT. 155-167 - Tao Liu, Xukun Zhang, Minghao Han, Lihua Zhang:
A Lightweight nnU-Net Combined with Target Adaptive Loss for Organs and Tumors Segmentation. 168-178 - JiChao Luo, Zhihong Chen, Wenbin Liu, Zaiyi Liu, Bingjiang Qiu, Gang Fang:
AdaptNet: Adaptive Learning from Partially Labeled Data for Abdomen Multi-organ and Tumor Segmentation. 179-193 - Hanwen Zhang, Yongzhi Huang, Bingding Huang:
Two-Stage Training for Abdominal Pan-Cancer Segmentation in Weak Label. 194-208 - Yuntao Zhu, Liwen Zou, Linyao Li, Pengxu Wen:
Selected Partially Labeled Learning for Abdominal Organ and Pan-Cancer Segmentation. 209-221 - Aneesh Rangnekar, Jue Jiang, Harini Veeraraghavan:
3D Swin Transformer for Partial Medical Auto Segmentation. 222-235 - Zhiyu Ye, Hairong Zheng, Tong Zhang:
Partial-Labeled Abdominal Organ and Cancer Segmentation via Cascaded Dual-Decoding U-Net. 236-252 - Yanbin Chen, Zhicheng Wu, Hao Chen, Mingjing Yang:
Conformer: A Parallel Segmentation Network Combining Swin Transformer and Convolutional Neutral Network. 253-266 - Youngbin Kong, Kwangtai Kim, Seoi Jeong, Kyu Eun Lee, Hyoun-Joong Kong:
Multi-Organ and Pan-Cancer Segmentation Framework from Partially Labeled Abdominal CT Datasets: Fine and Swift nnU-Nets with Label Fusion. 267-282 - Shoujin Huang, Huaishui Yang, Lifeng Mei, Tan Zhang, Shaojun Liu, Mengye Lyu:
From Whole-Body to Abdomen: Streamlined Segmentation of Organs and Tumors via Semi-Supervised Learning and Efficient Coarse-to-Fine Inference. 283-292 - Ziran Chen, Taiyu Han, Xueqiang Zeng, Guangtao Huang, Huihui Yang, Yan Kang:
Semi-supervised Abdominal Organ and Pan-Cancer Segmentation with Efficient nnU-Net. 293-305 - Zhiqiang Zhong, Rongxuan He, Deming Zhu, Mengqiu Tian, Songfeng Li:
Multi-task Learning with Iterative Training in Hybrid Labeling Dataset for Semi-supervised Abdominal Multi-organ and Tumor Segmentation. 306-318 - Peng An, Yurou Xu, Panpan Wu:
Attention Mechanism-Based Deep Supervision Network for Abdominal Multi-organ Segmentation. 319-332 - Chong Wang, Wen Dong, Rongjun Ge:
Teacher-Student Semi-supervised Strategy for Abdominal CT Organ Segmentation. 333-345 - Zengmin Zhang, Xiaomeng Duan, Yanjun Peng, Zhengyu Li:
A Semi-supervised Abdominal Multi-organ Pan-Cancer Segmentation Framework with Knowledge Distillation and Multi-label Fusion. 346-361
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