no code implementations • 23 Apr 2024 • Xingyue Zhao, Zhongyu Li, Xiangde Luo, Peiqi Li, Peng Huang, Jianwei Zhu, Yang Liu, Jihua Zhu, Meng Yang, Shi Chang, Jun Dong
Especially, an asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels, i. e., conservative labels and radical labels, to train two asymmetric segmentation networks simultaneously.
1 code implementation • 20 Mar 2024 • Yicheng Wu, Xiangde Luo, Zhe Xu, Xiaoqing Guo, Lie Ju, ZongYuan Ge, Wenjun Liao, Jianfei Cai
To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation.
1 code implementation • 15 Dec 2023 • Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
3 code implementations • 11 Oct 2023 • Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, Matthew Lungren, Lei Xing, Le Lu, Alan Yuille, Yuyin Zhou
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
1 code implementation • 23 Sep 2023 • Hongqiu Wang, Jian Chen, Shichen Zhang, Yuan He, Jinfeng Xu, Mengwan Wu, Jinlan He, Wenjun Liao, Xiangde Luo
We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach.
no code implementations • 18 Sep 2023 • Meng Han, Xiangde Luo, Wenjun Liao, Shichuan Zhang, Shaoting Zhang, Guotai Wang
Specifically, we employ a Triple-branch multi-Dilated network (TDNet) with one encoder and three decoders using different dilation rates to capture features from different receptive fields that are complementary to each other to generate high-quality soft pseudo labels.
no code implementations • 24 Aug 2023 • Ziqi Yang, Zhongyu Li, Chen Liu, Xiangde Luo, Xingguang Wang, Dou Xu, CHAOQUN LI, Xiaoying Qin, Meng Yang, Long Jin
To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification.
1 code implementation • 30 Jul 2023 • Zihan Li, Yuan Zheng, Xiangde Luo, Dandan Shan, Qingqi Hong
We evaluate ScribbleVC on three benchmark datasets and compare it with state-of-the-art methods.
Ranked #2 on Semantic Segmentation on ACDC Scribbles
1 code implementation • 29 Jun 2023 • Guotai Wang, Jianghao Wu, Xiangde Luo, Xinglong Liu, Kang Li, Shaoting Zhang
The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models.
1 code implementation • 19 Aug 2022 • Guotai Wang, Xiangde Luo, Ran Gu, Shuojue Yang, Yijie Qu, Shuwei Zhai, Qianfei Zhao, Kang Li, Shaoting Zhang
Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.
1 code implementation • 11 Aug 2022 • Shuwei Zhai, Guotai Wang, Xiangde Luo, Qiang Yue, Kang Li, Shaoting Zhang
The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire.
no code implementations • 8 Mar 2022 • Yunxiang Li, Ruilong Dan, Shuai Wang, Yifan Cao, Xiangde Luo, Chenghao Tan, Gangyong Jia, Huiyu Zhou, You Zhang, Yaqi Wang, Li Wang
For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to other datasets with different imaging parameters.
1 code implementation • 4 Mar 2022 • Xiangde Luo, Minhao Hu, Wenjun Liao, Shuwei Zhai, Tao Song, Guotai Wang, Shaoting Zhang
Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis, treatment planning, and following-up.
1 code implementation • 9 Dec 2021 • Xiangde Luo, Minhao Hu, Tao Song, Guotai Wang, Shaoting Zhang
Notably, this work may be the first attempt to combine CNN and transformer for semi-supervised medical image segmentation and achieve promising results on a public benchmark.
3 code implementations • 3 Nov 2021 • Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang
Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.
1 code implementation • 10 Jul 2021 • Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
1 code implementation • 20 May 2021 • Shaohua Li, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong liu, Rick Goh
Medical image segmentation is important for computer-aided diagnosis.
Ranked #1 on Brain Tumor Segmentation on BRATS 2019
2 code implementations • 25 Apr 2021 • Xiangde Luo, Guotai Wang, Tao Song, Jingyang Zhang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang
To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects.
1 code implementation • 12 Apr 2021 • Xiangde Luo, Tao Song, Guotai Wang, Jieneng Chen, Yinan Chen, Kang Li, Dimitris N. Metaxas, Shaoting Zhang
To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.
21 code implementations • 8 Feb 2021 • Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L. Yuille, Yuyin Zhou
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Ranked #4 on Medical Image Segmentation on ACDC
2 code implementations • 13 Dec 2020 • Xiangde Luo, Wenjun Liao, Jieneng Chen, Tao Song, Yinan Chen, Shichuan Zhang, Nianyong Chen, Guotai Wang, Shaoting Zhang
In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semi-supervised NPC GTV segmentation.
1 code implementation • 1 Nov 2020 • Xu Chen, Xiangde Luo, Yitian Zhao, Shaoting Zhang, Guotai Wang, Yalin Zheng
Inspired by Euler's Elastica model and recent active contour models introduced into the field of deep learning, we propose a novel active contour with elastica (ACE) loss function incorporating Elastica (curvature and length) and region information as geometrically-natural constraints for the image segmentation tasks.
1 code implementation • 9 Sep 2020 • Xiangde Luo, Jieneng Chen, Tao Song, Yinan Chen, Guotai Wang, Shaoting Zhang
Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target.