8 citations · 24 across the 27 of their papers we have counts for
15 papers · 1 filter
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…
Med-MMFL: A Multimodal Federated Learning Benchmark in Healthcare
Aavash Chhetri, Bibek Niroula, Pratik Shrestha +5
Federated learning (FL) enables collaborative model training across decentralized medical institutions while preserving data privacy. However, medical FL benchmarks remain scarce,…
Effect of Data Augmentation on Conformal Prediction for Diabetic Retinopathy
Rizwan Ahamed, Annahita Amireskandari, Joel Palko +3
The clinical deployment of deep learning models for high-stakes tasks such as diabetic retinopathy (DR) grading requires demonstrable reliability. While models achieve high accurac…
Addressing Bias in VLMs for Glaucoma Detection Without Protected Attribute Supervision
Ahsan Habib Akash, Greg Murray, Annahita Amireskandari +4
Vision-Language Models (VLMs) have achieved remarkable success on multimodal tasks such as image-text retrieval and zero-shot classification, yet they can exhibit demographic biase…
Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models
Bidur Khanal, Sandesh Pokhrel, Sanjay Bhandari +7
Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language. Existing VLMs demonstrate an i…
NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevance
Anju Chhetri, Jari Korhonen, Prashnna Gyawali +1
Ensuring reliability is paramount in deep learning, particularly within the domain of medical imaging, where diagnostic decisions often hinge on model outputs. The capacity to sepa…
TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision
Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez +3
Deep learning has significantly advanced the field of gastrointestinal vision, enhancing disease diagnosis capabilities. One major challenge in automating diagnosis within gastroin…
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities
Pranav Poudel, Prashant Shrestha, Sanskar Amgain +3
Multimodal AI has demonstrated superior performance over unimodal approaches by leveraging diverse data sources for more comprehensive analysis. However, applying this effectivenes…
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis
Jacob Thrasher, Alina Devkota, Ahmed Tafti +2
Alzheimer's Dementia (AD) represents one of the most pressing challenges in the field of neurodegenerative disorders, with its progression analysis being crucial for understanding…
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical Heterogeneity
Sanskar Amgain, Prashant Shrestha, Sophia Bano +5
Purpose: We apply federated learning to train an OCT image classifier simulating a realistic scenario with multiple clients and statistical heterogeneous data distribution where da…
Medical Vision Language Pretraining: A survey
Prashant Shrestha, Sanskar Amgain, Bidur Khanal +2
Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to the scarcity of labeled data in the medical domain. By leveraging paired/unpaired vision a…
TextAug: Test time Text Augmentation for Multimodal Person Re-identification
Mulham Fawakherji, Eduard Vazquez, Pasquale Giampa +1
Multimodal Person Reidentification is gaining popularity in the research community due to its effectiveness compared to counter-part unimodal frameworks. However, the bottleneck fo…
ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis
Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez +2
Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. Thi…