1 citations · 3 across the 20 of their papers we have counts for
20 papers
ReMoE: Report-Guided Mixture-of-Experts for Multimodal OCT/OCTA Anomaly Detection
Zihan Nie, Qincheng Qiao, Muhao Xu +4
Multimodal medical anomaly detection identifies samples deviating from normal patterns, where scarce abnormal cases make normality modeling from normal data practical. In retinal O…
CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
Zihan Nie, Muhao Xu, Wei Feng +8
Medical image anomaly detection is central to timely diagnosis and clinical decision support, yet abnormal samples are costly to collect because of disease rarity, privacy concerns…
Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decoding
Zhongxing Xu, Zhonghua Wang, Zhe Qian +10
Recent advancements in multimodal large reasoning models (MLRMs) have significantly improved performance in visual question answering. However, we observe that transition words (e.…
OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence
Feilong Tang, Xiang An, Yunyao Yan +16
Hypothesis. Artificial general intelligence is, at its core, a compression problem. Effective compression demands resonance: deep learning scales best when its architecture aligns…
A General Model for Retinal Segmentation and Quantification
Zhonghua Wang, Lie Ju, Sijia Li +12
Retinal imaging is fast, non-invasive, and widely available, offering quantifiable structural and vascular signals for ophthalmic and systemic health assessment. This accessibility…
PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics
Yaling Shen, Stephanie Fong, Yiwen Jiang +9
The increasing integration of large language models (LLMs) into mental health applications necessitates robust frameworks for evaluating professional safety alignment. Current eval…
A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Ming Hu, Chenglong Ma, Wei Li +117
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the compl…
StreamAgent: Towards Anticipatory Agents for Streaming Video Understanding
Haolin Yang, Feilong Tang, Lingxiao Zhao +10
Real-time streaming video understanding in domains such as autonomous driving and intelligent surveillance poses challenges beyond conventional offline video processing, requiring…
Towards Efficient Medical Reasoning with Minimal Fine-Tuning Data
Xinlin Zhuang, Feilong Tang, Haolin Yang +9
Supervised Fine-Tuning (SFT) of the language backbone plays a pivotal role in adapting Vision-Language Models (VLMs) to specialized domains such as medical reasoning. However, exis…
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
Feilong Tang, Chengzhi Liu, Zhongxing Xu +9
Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations…
Towards Dynamic 3D Reconstruction of Hand-Instrument Interaction in Ophthalmic Surgery
Ming Hu, Zhengdi Yu, Feilong Tang +7
Accurate 3D reconstruction of hands and instruments is critical for vision-based analysis of ophthalmic microsurgery, yet progress has been hampered by the lack of realistic, large…
PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation
Yiheng Zhong, Zihong Luo, Chengzhi Liu +7
Segment Anything Model (SAM) demonstrates powerful zero-shot capabilities; however, its accuracy and robustness significantly decrease when applied to medical image segmentation. E…
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Zhaodong Wu, Qiaochu Zhao, Ming Hu +13
With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and technology for the diagnosis and tre…
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Haochen Xue, Feilong Tang, Ming Hu +13
Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, th…
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos
Haolin Yang, Feilong Tang, Ming Hu +8
Video diffusion models (VDMs) facilitate the generation of high-quality videos, with current research predominantly concentrated on scaling efforts during training through improvem…
Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation
Xinkun Wang, Yifang Wang, Senwei Liang +7
This paper discusses how ophthalmologists often rely on multimodal data to improve diagnostic accuracy. However, complete multimodal data is rare in real-world applications due to…
Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Code
Yicheng Wu, Tao Song, Zhonghua Wu +5
Magnetic resonance imaging (MRI) is a powerful and versatile imaging technique, offering a wide spectrum of information about the anatomy by employing different acquisition modalit…
Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
Zhongxing Xu, Feilong Tang, Zhe Chen +5
The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research powerful cross-modal semantic understanding capabilitie…
Discriminating retinal microvascular and neuronal differences related to migraines: Deep Learning based Crossectional Study
Feilong Tang, Matt Trinh, Annita Duong +5
Migraine, a prevalent neurological disorder, has been associated with various ocular manifestations suggestive of neuronal and microvascular deficits. However, there is limited und…
Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation
Feilong Tang, Zhongxing Xu, Zhaojun Qu +3
Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate contextual knowledge to improve the completeness of class activation maps (CAM). In this work, w…