no code implementations • 7 Apr 2024 • Wei Fang, Yuxing Tang, Heng Guo, Mingze Yuan, Tony C. W. Mok, Ke Yan, Jiawen Yao, Xin Chen, Zaiyi Liu, Le Lu, Ling Zhang, Minfeng Xu
In the realm of medical 3D data, such as CT and MRI images, prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution.
no code implementations • 22 Mar 2024 • Heng Guo, Jianfeng Zhang, Jiaxing Huang, Tony C. W. Mok, Dazhou Guo, Ke Yan, Le Lu, Dakai Jin, Minfeng Xu
In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D.
no code implementations • 28 Jul 2023 • Ke Yan, Dakai Jin, Dazhou Guo, Minfeng Xu, Na Shen, Xian-Sheng Hua, Xianghua Ye, Le Lu
Motivated by this observation, we propose a novel end-to-end framework to improve LN detection performance by leveraging their station information.
no code implementations • 20 Jul 2023 • Jianpeng Zhang, Xianghua Ye, Jianfeng Zhang, Yuxing Tang, Minfeng Xu, Jianfei Guo, Xin Chen, Zaiyi Liu, Jingren Zhou, Le Lu, Ling Zhang
In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules.
no code implementations • 7 Jul 2023 • Xiaoyu Bai, Fan Bai, Xiaofei Huo, Jia Ge, Tony C. W. Mok, Zi Li, Minfeng Xu, Jingren Zhou, Le Lu, Dakai Jin, Xianghua Ye, JingJing Lu, Ke Yan
We then use this SAM to identify corresponding regions on paired images using robust grid-points matching, followed by a point-set based affine/rigid registration, and a deformable fine-tuning step to produce registered paired images.
1 code implementation • ICCV 2023 • Yankai Jiang, Mingze Sun, Heng Guo, Xiaoyu Bai, Ke Yan, Le Lu, Minfeng Xu
Alice introduces a new contrastive learning strategy which encourages the similarity between views that are diversely mined but with consistent high-level semantics, in order to learn invariant anatomical features.
no code implementations • 1 Feb 2023 • Zhanghexuan Ji, Dazhou Guo, Puyang Wang, Ke Yan, Le Lu, Minfeng Xu, Jingren Zhou, Qifeng Wang, Jia Ge, Mingchen Gao, Xianghua Ye, Dakai Jin
Deep learning empowers the mainstream medical image segmentation methods.
no code implementations • ICCV 2023 • Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, Alan Yuille, Zaiyi Liu, Ling Zhang
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases.
no code implementations • ICCV 2023 • Zhanghexuan Ji, Dazhou Guo, Puyang Wang, Ke Yan, Le Lu, Minfeng Xu, Qifeng Wang, Jia Ge, Mingchen Gao, Xianghua Ye, Dakai Jin
In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs.
1 code implementation • 5 Dec 2022 • Heng Guo, Jianfeng Zhang, Ke Yan, Le Lu, Minfeng Xu
For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs.
no code implementations • 2 Aug 2022 • Minfeng Xu, Heng Guo, Jianfeng Zhang, Ke Yan, Le Lu
Accurate and robust abdominal multi-organ segmentation from CT imaging of different modalities is a challenging task due to complex inter- and intra-organ shape and appearance variations among abdominal organs.
2 code implementations • 21 Sep 2021 • Yicheng Wu, ZongYuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong Xia, Jianfei Cai
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.
3 code implementations • 4 Mar 2021 • Yicheng Wu, Minfeng Xu, ZongYuan Ge, Jianfei Cai, Lei Zhang
Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions.