20 papers
iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data
Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali +2
Multimodal learning of images and tabular data is often impaired by ineffective representations, resulting in redundancy, dispersion, and generalization problems. To tackle this ch…
Naming the Concepts Classifiers Rely On: Language-Anchored Decomposition for Faithful Explanation
Ahsan Habib Akash, Dipkamal Bhusal, Stacey Jones +3
Deep neural networks are widely deployed in high-stakes visual applications where interpretability is critical, yet existing explanations face a trade-off: post-hoc concept methods…
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
Aavash Chhetri, Bibek Niroula, Eduard Vazquez +4
In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or perform naive feature synthes…
A Benchmark for Hallucination Detection in VLMs for Gastrointestinal Endoscopy
Aminu Lawal, Niyoj Oli, Sachin Acharya +3
Vision-language models (VLMs) are prone to hallucination, which remains a major barrier to their safe deployment in clinical practice. To date, most hallucination detection methods…
When Confidence Lacks Concepts: Interpretable OOD Detection via Representation Perturbations
Anju Chhetri, Pratik Shrestha, Ramesh Rana +3
Deep neural networks have achieved remarkable performance across medical imaging tasks, yet their tendency to overgeneralize under distributional shifts poses a major obstacle to s…
SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis
Niyoj Oli, Sachin Acharya, Sandesh Pokhrel +7
Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions and lifestyle habits. However, ea…