3 citations · 11 across the 32 of their papers we have counts for
14 papers · 1 filter
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…
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…
ImageRAGTurbo: Towards One-step Text-to-Image Generation with Retrieval-Augmented Diffusion Models
Peijie Qiu, Hariharan Ramshankar, Arnau Ramisa +4
Diffusion models have emerged as the leading approach for text-to-image generation. However, their iterative sampling process, which gradually morphs random noise into coherent ima…
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.…
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…
RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection
Hao Wang, Wenhui Zhu, Jiayou Qin +5
Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for…
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…
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…
PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Wenhui Zhu, Peijie Qiu, Xiwen Chen +2
Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to thei…
TetCNN: Convolutional Neural Networks on Tetrahedral Meshes
Mohammad Farazi, Zhangsihao Yang, Wenhui Zhu +2
Convolutional neural networks (CNN) have been broadly studied on images, videos, graphs, and triangular meshes. However, it has seldom been studied on tetrahedral meshes. Given the…