3 citations · 3 across the 15 of their papers we have counts for
17 papers
Anatomy-aware Fine-grained Multimodal Fusion for Laryngopharyngeal Cancer T-Staging Prediction Using CT and Radiology Report
Xingyue Zhao, Yanzhou Su, Fang Zhang +12
Accurate T-staging is crucial for guiding personalized treatment strategies for laryngopharyngeal cancer. However, current clinical practice relies on invasive biopsy procedures, w…
Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Linghao Zhuang +11
Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Exis…
PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement
Zilong Li, Dongyang Li, Chenglong Ma +6
Contrast-enhanced computed tomography (CECT) is pivotal for highlighting tissue perfusion and vascularity, yet its clinical ubiquity is impeded by the invasive nature of contrast a…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
MUSE: Multi-Scale Dense Self-Distillation for Nucleus Detection and Classification
Zijiang Yang, Hanqing Chao, Bokai Zhao +10
Nucleus detection and classification (NDC) in histopathology analysis is a fundamental task that underpins a wide range of high-level pathology applications. However, existing meth…
Vision-Language Models for Automated 3D PET/CT Report Generation
Wenpei Jiao, Kun Shang, Hui Li +8
Positron emission tomography/computed tomography (PET/CT) is essential in oncology, yet the rapid expansion of scanners has outpaced the availability of trained specialists, making…
Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning
Runze Wang, Zeli Chen, Zhiyun Song +8
To reduce radiation exposure and improve the diagnostic efficacy of low-dose computed tomography (LDCT), numerous deep learning-based denoising methods have been developed to mitig…
HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss
Yi Huang, Ke Zhang, Wei Liu +6
Accurate segmentation of tubular structures in medical images, such as vessels and airway trees, is crucial for computer-aided diagnosis, radiotherapy, and surgical planning. Howev…
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,…
From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans
Qinji Yu, Yirui Wang, Ke Yan +11
Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging, treatment planning and beyond. Identifying scatte…
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…
From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer
Zijiang Yang, Zhongwei Qiu, Tiancheng Lin +13
It is clinically crucial and potentially very beneficial to be able to analyze and model directly the spatial distributions of cells in histopathology whole slide images (WSI). How…
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba
Zhongwei Qiu, Hanqing Chao, Tiancheng Lin +12
Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, th…
Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation
Vince Zhu, Zhanghexuan Ji, Dazhou Guo +6
Deep segmentation networks achieve high performance when trained on specific datasets. However, in clinical practice, it is often desirable that pretrained segmentation models can…
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images
Zhongwei Qiu, Hanqing Chao, Wenbin Liu +6
Survival analysis using pathology images poses a considerable challenge, as it requires the localization of relevant information from the multitude of tiles within whole slide imag…
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer
Yirui Wang, Qinji Yu, Ke Yan +9
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for…
Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans
Heng Guo, Jianfeng Zhang, Jiaxing Huang +6
Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. However, its direct adaptation in medical image segmentation tasks shows sign…