3 citations · 12 across the 42 of their papers we have counts for
23 papers · 1 filter
Does Your ViT Still Need U-Net for Segmentation?
Xin Li, Wenhui Zhu, Xuanzhao Dong +6
Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of convolutional networks thro…
Mags-RL: Wearing Multimodal LLMs a Magnifying Glass via Agentic Reinforcement Learning For Complex Scene Reasoning
Xuanzhao Dong, Wenhui Zhu, Peijie Qiu +11
Despite their popularity and success, Multimodal Large Language Models (MLLMs) often struggle to interpret images accurately, which limits their reasoning capability in complex sce…
OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models
Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +13
The advancement of general medical Multimodal Large Language Models (MLLMs) has shown great potential for building conversational assistants to support clinical diagnosis. However,…
Hierarchical Mesh Transformers with Topology-Guided Pretraining for Morphometric Analysis of Brain Structures
Yujian Xiong, Mohammad Farazi, Yanxi Chen +8
Representation learning on large-scale unstructured volumetric and surface meshes poses significant challenges in neuroimaging, especially when models must incorporate diverse vert…
Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement
Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +8
Over the past decade, generative models have demonstrated success in enhancing fundus images. However, the evaluation of these models remains a challenge. A benchmark for fundus im…
OTPrune: Distribution-Aligned Visual Token Pruning via Optimal Transport
Xiwen Chen, Wenhui Zhu, Gen Li +9
Multi-modal large language models (MLLMs) achieve strong visual-language reasoning but suffer from high inference cost due to redundant visual tokens. Recent work explores visual t…
SGW-GAN: Sliced Gromov-Wasserstein Guided GANs for Retinal Fundus Image Enhancement
Yujian Xiong, Xuanzhao Dong, Wenhui Zhu +3
Retinal fundus photography is indispensable for ophthalmic screening and diagnosis, yet image quality is often degraded by noise, artifacts, and uneven illumination. Recent GAN- an…
Fast 2DGS: Efficient Image Representation with Deep Gaussian Prior
Hao Wang, Ashish Bastola, Chaoyi Zhou +5
As generative models become increasingly capable of producing high-fidelity visual content, the demand for efficient, interpretable, and editable image representations has grown su…
nnMobileNet++: Towards Efficient Hybrid Networks for Retinal Image Analysis
Xin Li, Wenhui Zhu, Xuanzhao Dong +4
Retinal imaging is a critical, non-invasive modality for the early detection and monitoring of ocular and systemic diseases. Deep learning, particularly convolutional neural networ…
VAOT: Vessel-Aware Optimal Transport for Retinal Fundus Enhancement
Xuanzhao Dong, Wenhui Zhu, Yujian Xiong +8
Color fundus photography (CFP) is central to diagnosing and monitoring retinal disease, yet its acquisition variability (e.g., illumination changes) often degrades image quality, w…
EVTP-IVS: Effective Visual Token Pruning For Unifying Instruction Visual Segmentation In Multi-Modal Large Language Models
Wenhui Zhu, Xiwen Chen, Zhipeng Wang +5
Instructed Visual Segmentation (IVS) tasks require segmenting objects in images or videos based on natural language instructions. While recent multimodal large language models (MLL…
LLaDA-MedV: Exploring Large Language Diffusion Models for Biomedical Image Understanding
Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +5
Autoregressive models (ARMs) have long dominated the landscape of biomedical vision-language models (VLMs). Recently, masked diffusion models such as LLaDA have emerged as promisin…
How Effective Can Dropout Be in Multiple Instance Learning ?
Wenhui Zhu, Peijie Qiu, Xiwen Chen +4
Multiple Instance Learning (MIL) is a popular weakly-supervised method for various applications, with a particular interest in histological whole slide image (WSI) classification.…
Schrödinger Diffusion Driven Signal Recovery in 3T BOLD fMRI Using Unmatched 7T Observations
Yujian Xiong, Xuanzhao Dong, Sebastian Waz +4
Ultra-high-field (7 Tesla) BOLD fMRI offers exceptional detail in both spatial and temporal domains, along with robust signal-to-noise characteristics, making it a powerful modalit…
A BERT-Style Self-Supervised Learning CNN for Disease Identification from Retinal Images
Xin Li, Wenhui Zhu, Peijie Qiu +3
In the field of medical imaging, the advent of deep learning, especially the application of convolutional neural networks (CNNs) has revolutionized the analysis and interpretation…
RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
Wenhui Zhu, Xin Li, Xiwen Chen +8
Recently, Multimodal Large Language Models (MLLMs) have gained significant attention for their remarkable ability to process and analyze non-textual data, such as images, videos, a…
Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model Adaptation
Xiwen Chen, Wenhui Zhu, Peijie Qiu +6
Vision-language models (VLMs) such as CLIP demonstrate strong performance but struggle when adapted to downstream tasks. Prompt learning has emerged as an efficient and effective s…
Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification
Hao Wang, Wenhui Zhu, Xuanzhao Dong +9
In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challeng…
AMG: Avatar Motion Guided Video Generation
Zhangsihao Yang, Mengyi Shan, Mohammad Farazi +4
Human video generation task has gained significant attention with the advancement of deep generative models. Generating realistic videos with human movements is challenging in natu…
Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters
Xiwen Chen, Wenhui Zhu, Peijie Qiu +1
Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed…
Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning
Yujian Xiong, Wenhui Zhu, Zhong-Lin Lu +1
The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechani…
FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion
Hao Wang, Sayed Pedram Haeri Boroujeni, Xiwen Chen +4
Wildfires are a significant threat to ecosystems and human infrastructure, leading to widespread destruction and environmental degradation. Recent advancements in deep learning and…
SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification
Peijie Qiu, Pan Xiao, Wenhui Zhu +2
Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. Typical MIL methods include a feature embedding part, which embed…