Search Results for author: Xianghua Ye

Found 19 papers, 3 papers with code

UAE: Universal Anatomical Embedding on Multi-modality Medical Images

1 code implementation25 Nov 2023 Xiaoyu Bai, Fan Bai, Xiaofei Huo, Jia Ge, JingJing Lu, Xianghua Ye, Ke Yan, Yong Xia

They use self-supervised learning to acquire a discriminative embedding for each voxel within the image.

Self-Supervised Learning

SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation

1 code implementation25 Nov 2023 Lin Tian, Zi Li, Fengze Liu, Xiaoyu Bai, Jia Ge, Le Lu, Marc Niethammer, Xianghua Ye, Ke Yan, Daikai Jin

In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration building on top of a Self-supervised Anatomical eMbedding (SAM) algorithm, which is capable of computing dense anatomical correspondences between two images at the voxel level.

Image Registration Medical Image Registration

Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification

no code implementations28 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.

Anatomy Multi-Task Learning

Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists

no code implementations20 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.

Decision Making

SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid

1 code implementation19 Jul 2023 Zi Li, Lin Tian, Tony C. W. Mok, Xiaoyu Bai, Puyang Wang, Jia Ge, Jingren Zhou, Le Lu, Xianghua Ye, Ke Yan, Dakai Jin

Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens.

Image Registration

Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images

no code implementations7 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.

Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning

no code implementations15 Jun 2023 Puyang Wang, Dazhou Guo, Dandan Zheng, Minghui Zhang, Haogang Yu, Xin Sun, Jia Ge, Yun Gu, Le Lu, Xianghua Ye, Dakai Jin

Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer.

Anatomy Computed Tomography (CT) +3

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

no code implementations27 Aug 2020 Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung-Ying Ho, Xianghua Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, Alan Yuille, Le Lu

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

no code implementations27 May 2020 Zhuotun Zhu, Ke Yan, Dakai Jin, Jinzheng Cai, Tsung-Ying Ho, Adam P. Harrison, Dazhou Guo, Chun-Hung Chao, Xianghua Ye, Jing Xiao, Alan Yuille, Le Lu

We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.

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