36 citations · 55 across the 18 of their papers we have counts for
13 papers · 1 filter
VIHD: Visual Intervention-based Hallucination Detection for Medical Visual Question Answering
Jiayi Chen, Benteng Ma, Zehui Liao +3
While medical Multimodal Large Language Models (MLLMs) have shown promise in assisting diagnosis, they still frequently generate hallucinated responses that appear linguistically p…
V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering
Mengyuan Jin, Zehui Liao, Yong Xia
Multimodal Large Language Models (MLLMs) have shown remarkable capability in assisting disease diagnosis in medical visual question answering (VQA). However, their outputs remain v…
Cycle Context Verification for In-Context Medical Image Segmentation
Shishuai Hu, Zehui Liao, Liangli Zhen +2
In-context learning (ICL) is emerging as a promising technique for achieving universal medical image segmentation, where a variety of objects of interest across imaging modalities…
UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Models
Zehui Liao, Shishuai Hu, Ke Zou +5
Vision-language models (VLMs) have great potential for medical image understanding, particularly in Visual Report Generation (VRG) and Visual Question Answering (VQA), but they may…
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification
Zehui Liao, Shishuai Hu, Yanning Zhang +1
Addressing mixed closed-set and open-set label noise in medical image classification remains a largely unexplored challenge. Unlike natural image classification, which often separa…
Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation
Shishuai Hu, Zehui Liao, Zeyou Liu +1
Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers, largely due to the discrepancies in data dist…
URL: Combating Label Noise for Lung Nodule Malignancy Grading
Xianze Ai, Zehui Liao, Yong Xia
Due to the complexity of annotation and inter-annotator variability, most lung nodule malignancy grading datasets contain label noise, which inevitably degrades the performance and…
Transformer-based Annotation Bias-aware Medical Image Segmentation
Zehui Liao, Yutong Xie, Shishuai Hu +1
Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have sug…
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
Shishuai Hu, Zehui Liao, Yong Xia
Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adapt…
Instance-dependent Label Distribution Estimation for Learning with Label Noise
Zehui Liao, Shishuai Hu, Yutong Xie +1
Noise transition matrix (NTM) estimation is a promising approach for learning with label noise. It can infer clean posterior probabilities, known as Label Distribution (LD), based…
ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation
Shishuai Hu, Zehui Liao, Yong Xia
The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinica…
Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
Zehui Liao, Shishuai Hu, Yutong Xie +1
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks…
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation
Yutong Xie, Jianpeng Zhang, Zehui Liao +2
It has been widely recognized that the success of deep learning in image segmentation relies overwhelmingly on a myriad amount of densely annotated training data, which, however, a…