36 citations · 39 across the 12 of their papers we have counts for
12 papers
A Continual Learning-driven Model for Accurate and Generalizable Segmentation of Clinically Comprehensive and Fine-grained Whole-body Anatomies in CT
Dazhou Guo, Zhanghexuan Ji, Yanzhou Su +31
Precision medicine in the quantitative management of chronic diseases and oncology would be greatly improved if the Computed Tomography (CT) scan of any patient could be segmented,…
OncoReg: Medical Image Registration for Oncological Challenges
Wiebke Heyer, Yannic Elser, Lennart Berkel +16
In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue…
UniReg: Conditional Unified Model for Medical Image Registration
Zi Li, Jianpeng Zhang, Tai Ma +7
Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However, existing approaches suffer fro…
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT
Zi Li, Ying Chen, Zeli Chen +12
In the radiation therapy of nasopharyngeal carcinoma (NPC), clinicians typically delineate the gross tumor volume (GTV) using non-contrast planning computed tomography to ensure ac…
A Deep Learning System for Rapid and Accurate Warning of Acute Aortic Syndrome on Non-contrast CT in China
Yujian Hu, Yilang Xiang, Yan-Jie Zhou +41
The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) is the imag…
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models
Weiwei Cao, Jianpeng Zhang, Yingda Xia +7
Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However, the lack of extensively annotated large-scale multi-disease datasets has hinder…
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
Tony C. W. Mok, Zi Li, Yunhao Bai +9
Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-gu…
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
Lin Tian, Zi Li, Fengze Liu +7
Image registration is a fundamental medical image analysis task. Ideally, registration should focus on aligning semantically corresponding voxels, i.e., the same anatomical locatio…
Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images
Xiaoyu Bai, Fan Bai, Xiaofei Huo +10
Radiotherapists require accurate registration of MR/CT images to effectively use information from both modalities. In a typical registration pipeline, rigid or affine transformatio…
SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid
Zi Li, Lin Tian, Tony C. W. Mok +8
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. Mo…
Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions
Xin Fan, Zi Li, Ziyang Li +4
Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimiz…
Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond
Risheng Liu, Zi Li, Xin Fan +3
Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contras…