3 citations · 10 across the 15 of their papers we have counts for
18 papers
Visual Semantic Entropy: Do Vision Language Models Recognize Visual Ambiguity?
Ta Duc Huy, Trang Nguyen, Townim Chowdhury +5
Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropy-based methods, such as Semantic Entropy (SE), rel…
Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinations
Tuan Dung Nguyen, Minh Khoi Ho, Qi Chen +8
Large vision-language models (LVLMs) achieve strong performance on visual reasoning tasks but remain highly susceptible to hallucination. Existing detection methods predominantly r…
Overthinking Causes Hallucination: Tracing Confounder Propagation in Vision Language Models
Abin Shoby, Ta Duc Huy, Tuan Dung Nguyen +6
Vision Language models (VLMs) often hallucinate non-existent objects. Detecting hallucination is analogous to detecting deception: a single final statement is insufficient, one mus…
DGRNet: Disagreement-Guided Refinement for Uncertainty-Aware Brain Tumor Segmentation
Bahram Mohammadi, Yanqiu Wu, Vu Minh Hieu Phan +6
Accurate brain tumor segmentation from MRI scans is critical for diagnosis and treatment planning. Despite the strong performance of recent deep learning approaches, two fundamenta…
Hierarchical Text-Guided Brain Tumor Segmentation via Sub-Region-Aware Prompts
Bahram Mohammadi, Ta Duc Huy, Afrouz Sheikholeslami +8
Brain tumor segmentation remains challenging because the three standard sub-regions, i.e., whole tumor (WT), tumor core (TC), and enhancing tumor (ET), often exhibit ambiguous visu…
Looking in the mirror: A faithful counterfactual explanation method for interpreting deep image classification models
Townim Faisal Chowdhury, Vu Minh Hieu Phan, Kewen Liao +5
Counterfactual explanations (CFE) for deep image classifiers aim to reveal how minimal input changes lead to different model decisions, providing critical insights for model interp…
Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical Reasoning
Jinquan Guan, Qi Chen, Lizhou Liang +5
Artificial intelligence (AI)-based chest X-ray (CXR) interpretation assistants have demonstrated significant progress and are increasingly being applied in clinical settings. Howev…
Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding
Ta Duc Huy, Duy Anh Huynh, Yutong Xie +10
Visual grounding (VG) is the capability to identify the specific regions in an image associated with a particular text description. In medical imaging, VG enhances interpretability…
Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs
Dung Nguyen, Minh Khoi Ho, Huy Ta +11
Medical Large Multi-modal Models (LMMs) have demonstrated remarkable capabilities in medical data interpretation. However, these models frequently generate hallucinations contradic…
Interactive Medical Image Analysis with Concept-based Similarity Reasoning
Ta Duc Huy, Sen Kim Tran, Phan Nguyen +7
The ability to interpret and intervene model decisions is important for the adoption of computer-aided diagnosis methods in clinical workflows. Recent concept-based methods link th…
ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer
Xuyin Qi, Zeyu Zhang, Aaron Berliano Handoko +11
Prostate cancer, a growing global health concern, necessitates precise diagnostic tools, with Magnetic Resonance Imaging (MRI) offering high-resolution soft tissue imaging that sig…
A Survey of Medical Vision-and-Language Applications and Their Techniques
Qi Chen, Ruoshan Zhao, Sinuo Wang +9
Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data.…
TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation
Anh-Dzung Doan, Vu Minh Hieu Phan, Surabhi Gupta +3
Infrared imaging offers resilience against changing lighting conditions by capturing object temperatures. Yet, in few scenarios, its lack of visual details compared to daytime visi…
AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis
Townim F. Chowdhury, Vu Minh Hieu Phan, Kewen Liao +5
The integration of vision-language models such as CLIP and Concept Bottleneck Models (CBMs) offers a promising approach to explaining deep neural network (DNN) decisions using conc…
MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training
Biao Wu, Yutong Xie, Zeyu Zhang +4
Vision-and-language pretraining (VLP) in the medical field utilizes contrastive learning on image-text pairs to achieve effective transfer across tasks. Yet, current VLP approaches…
Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis
Vu Minh Hieu Phan, Yutong Xie, Bowen Zhang +6
Unpaired medical image synthesis aims to provide complementary information for an accurate clinical diagnostics, and address challenges in obtaining aligned multi-modal medical sca…
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Townim Faisal Chowdhury, Kewen Liao, Vu Minh Hieu Phan +7
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretab…
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Vu Minh Hieu Phan, Yutong Xie, Yuankai Qi +7
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descript…