10 citations · 16 across the 11 of their papers we have counts for
11 papers
Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA
Sushant Gautam, Vajira Thambawita, Michael A. Riegler +2
Healthcare multimodal AI must combine visual and textual evidence while remaining reliable and interpretable. Using MediaEval Medico 2025 as a retrospective GI endoscopy case study…
Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry
Oriana Presacan, Andreea Grama, Larisa Irimină +6
Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving. When the available information is insu…
Steering grids for sparse-autoencoder features: when a top-context label names an activation regime rather than a causal axis
Michael A. Riegler, Birk Sebastian Frostelid Torpmann-Hagen
The standard protocol for interpreting sparse-autoencoder (SAE) features labels each feature from its top-activating contexts and validates the label by steering that single featur…
VideoHEDGE: Entropy-Based Hallucination Detection for Video-VLMs via Semantic Clustering and Spatiotemporal Perturbations
Sushant Gautam, Cise Midoglu, Vajira Thambawita +2
Hallucinations in video-capable vision-language models (Video-VLMs) remain frequent and high-confidence, while existing uncertainty metrics often fail to align with correctness. We…
HEDGE: Hallucination Estimation via Dense Geometric Entropy for VQA with Vision-Language Models
Sushant Gautam, Michael A. Riegler, Pål Halvorsen
Vision-language models (VLMs) enable open-ended visual question answering but remain prone to hallucinations. We present HEDGE, a unified framework for hallucination detection that…
Using Large Language Models to Suggest Informative Prior Distributions in Bayesian Statistics
Michael A. Riegler, Kristoffer Herland Hellton, Vajira Thambawita +1
Selecting prior distributions in Bayesian statistics is challenging, resource-intensive, and subjective. We analyze using large-language models (LLMs) to suggest suitable, knowledg…
Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy
Sushant Gautam, Michael A. Riegler, Pål Halvorsen
Medical Visual Question Answering (MedVQA) is a promising field for developing clinical decision support systems, yet progress is often limited by the available datasets, which can…
Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models
Sushant Gautam, Michael A. Riegler, Pål Halvorsen
We investigate fine-tuning Vision-Language Models (VLMs) for multi-task medical image understanding, focusing on detection, localization, and counting of findings in medical images…
SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding
Sushant Gautam, Cise Midoglu, Vajira Thambawita +3
The integration of artificial intelligence in sports analytics has transformed soccer video understanding, enabling real-time, automated insights into complex game dynamics. Tradit…
Merging synthetic and real embryo data for advanced AI predictions
Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita +5
Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance th…
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma +19
Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparatio…