6 citations · 26 across the 11 of their papers we have counts for
12 papers
EH-Benchmark Ophthalmic Hallucination Benchmark and Agent-Driven Top-Down Traceable Reasoning Workflow
Xiaoyu Pan, Yang Bai, Ke Zou +5
Medical Large Language Models (MLLMs) play a crucial role in ophthalmic diagnosis, holding significant potential to address vision-threatening diseases. However, their accuracy is…
Can OpenAI o1 Reason Well in Ophthalmology? A 6,990-Question Head-to-Head Evaluation Study
Sahana Srinivasan, Xuguang Ai, Minjie Zou +14
Question: What is the performance and reasoning ability of OpenAI o1 compared to other large language models in addressing ophthalmology-specific questions? Findings: This study ev…
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis
Bo Liu, Ke Zou, Liming Zhan +7
Medical Visual Question Answering (Med-VQA) combines computer vision and natural language processing to automatically answer clinical inquiries about medical images. However, curre…
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
Zhenyue Qin, Yu Yin, Dylan Campbell +6
The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment. Large vision-…
MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM
Nan Zhou, Ke Zou, Kai Ren +7
The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to…
Confidence-aware multi-modality learning for eye disease screening
Ke Zou, Tian Lin, Zongbo Han +6
Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performa…
Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices
Hongye Zeng, Ke Zou, Zhihao Chen +10
Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the mod…
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Meng Wang, Tian Lin, Lianyu Wang +21
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classific…
Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation
Kai Ren, Ke Zou, Xianjie Liu +5
Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual…
Reliable Multimodality Eye Disease Screening via Mixture of Student's t Distributions
Ke Zou, Tian Lin, Xuedong Yuan +4
Multimodality eye disease screening is crucial in ophthalmology as it integrates information from diverse sources to complement their respective performances. However, the existing…
A Review of Uncertainty Estimation and its Application in Medical Imaging
Ke Zou, Zhihao Chen, Xuedong Yuan +3
The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliabili…
TBraTS: Trusted Brain Tumor Segmentation
Ke Zou, Xuedong Yuan, Xiaojing Shen +2
Despite recent improvements in the accuracy of brain tumor segmentation, the results still exhibit low levels of confidence and robustness. Uncertainty estimation is one effective…