10 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…
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…
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…
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…
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…