175 citations · 925 across the 45 of their papers we have counts for
48 papers
GWDM: A Guided Wavelet Diffusion Model for Realistic Histopathology Image Generation
Abdullah, Tao Huang, Ickjai Lee +2
Histopathology images are recognized as a reliable and comprehensive modality for Deep Learning (DL) based diagnosis. However, effectiveness is constrained by limited large-scale a…
Representation: Geometry Supervised Representation Learning of Phenotypes via Counterfactual Reasoning for Medical VLMs
Hao Wang, Qiwei Zeng, Jinghao Lin +6
Medical vision-language models (VLMs) have shown increasing potential for radiological image interpretation. Medical VLMs encode radiological images into visual representations tha…
Geometry-Supervised Visual Representation Learning for Multi-Phenotype Lesion Interpretation in Medical VLMs
Hao Wang, Qiwei Zeng, Shuchang Ye +5
Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation. However, these models still struggle to interpret multi-phenotype lesions w…
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Mingyuan Meng, Shuchang Ye, Mingjian Li +3
Multi-modal medical images and clinical reports provide complementary anatomical, functional, and semantic information for medical image segmentation. Effectively exploiting these…
Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation
Songyue Han, Mingye Zou, Shuchang Ye +2
Medical image segmentation is essential for modern computer-aided medicine. Recently, text-guided segmentation has shown promise by incorporating clinician-formulated textual repor…
EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models
Qiwei Zeng, Hao Wang, Jinghao Lin +6
Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generation. However, their practical…
ImProNCDE: Impulse-Corrected Neural Controlled Differential Equations with Prototype Learning for Longitudinal Prognosis Prediction
Hao Wang, Yupeng Xu, Jinghao Lin +5
Longitudinal ophthalmic imaging analysis is an essential step for prognosis prediction in ophthalmic diseases. However, AI-assisted prognosis models are challenged by follow-up seq…
DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction
Yi Huang, Lei Bi, Jinman Kim
Glaucoma is a leading cause of irreversible blindness worldwide, and early detection from fundus images is critical for effective disease management. While deep learning has achiev…
HyperWalker: Dynamic Hypergraph-Based Deep Diagnosis for Multi-Hop Clinical Modeling across EHR and X-Ray in Medical VLMs
Yuezhe Yang, Hao Wang, Yige Peng +2
Automated clinical diagnosis remains a core challenge in medical AI, which usually requires models to integrate multi-modal data and reason across complex, case-specific contexts.…
Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention
Mingyuan Meng, Michael Fulham, Lei Bi +1
Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for…
Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies
Yuyu Guo, Lei Bi, Zhengbin Zhu +4
Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (multiple time points) is a fundamental requirement for quantitative analysis of its str…
Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions
Lei Bi, Michael Fulham, Jinman Kim
Automatic skin lesion segmentation methods based on fully convolutional networks (FCNs) are regarded as the state-of-the-art for accuracy. When there are, however, insufficient tra…
Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments
Mingjian Li, Mingyuan Meng, Shuchang Ye +4
Medical image segmentation is a fundamental task in numerous medical engineering applications. Recently, language-guided segmentation has shown promise in medical scenarios where t…
Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration
Mingyuan Meng, Dagan Feng, Lei Bi +1
Deformable image registration is a fundamental step for medical image analysis. Recently, transformers have been used for registration and outperformed Convolutional Neural Network…
Enhancing medical vision-language contrastive learning via inter-matching relation modelling
Mingjian Li, Mingyuan Meng, Michael Fulham +3
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-t…
Self-Supervised Multi-Modality Learning for Multi-Label Skin Lesion Classification
Hao Wang, Euijoon Ahn, Lei Bi +1
The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide a detailed view of the surface structures whereas cli…
Full-resolution MLPs Empower Medical Dense Prediction
Mingyuan Meng, Yuxin Xue, Dagan Feng +2
Dense prediction is a fundamental requirement for many medical vision tasks such as medical image restoration, registration, and segmentation. The most popular vision model, Convol…
PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network
Yuxin Xue, Lei Bi, Yige Peng +3
Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but als…
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
Mingyuan Meng, Michael Fulham, Dagan Feng +2
Deformable image registration aims to find a dense non-linear spatial correspondence between a pair of images, which is a crucial step for many medical tasks such as tumor growth m…
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
Mingyuan Meng, Lei Bi, Michael Fulham +2
Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and med…
Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
Mingyuan Meng, Lei Bi, Michael Fulham +2
Image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to p…
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
Mingyuan Meng, Bingxin Gu, Michael Fulham +4
Survival prediction is a major concern for cancer management. Deep survival models based on deep learning have been widely adopted to perform end-to-end survival prediction from me…
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images
Yuxin Xue, Yige Peng, Lei Bi +2
Positron emission tomography (PET) is the most sensitive molecular imaging modality routinely applied in our modern healthcare. High radioactivity caused by the injected tracer dos…
Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision
Mingyuan Meng, Lei Bi, Dagan Feng +1
In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of…