29 citations · 91 across the 48 of their papers we have counts for
21 papers · 1 filter
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…
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…
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,…
CURE: Centroid-guided Unsupervised Representation Erasure for Facial Recognition Systems
Fnu Shivam, Nima Najafzadeh, Yenumula Reddy +1
In the current digital era, facial recognition systems offer significant utility and have been widely integrated into modern technological infrastructures; however, their widesprea…
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…
Federated Foundation Model for GI Endoscopy Images
Alina Devkota, Annahita Amireskandari, Joel Palko +5
Gastrointestinal (GI) endoscopy is essential in identifying GI tract abnormalities in order to detect diseases in their early stages and improve patient outcomes. Although deep lea…
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…
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision
Sandesh Pokhrel, Sanjay Bhandari, Sharib Ali +7
The integration of deep learning tools in gastrointestinal vision holds the potential for significant advancements in diagnosis, treatment, and overall patient care. A major challe…
Enhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques
Jacob Thrasher, Annahita Amireskandari, Prashnna Gyawali
Eye diseases are common in older Americans and can lead to decreased vision and blindness. Recent advancements in imaging technologies allow clinicians to capture high-quality imag…
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…
Segmentation of Maya hieroglyphs through fine-tuned foundation models
FNU Shivam, Megan Leight, Mary Kate Kelly +5
The study of Maya hieroglyphic writing unlocks the rich history of cultural and societal knowledge embedded within this ancient civilization's visual narrative. Artificial Intellig…
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation
Nilesh Kumar, Prashnna K. Gyawali, Sandesh Ghimire +1
Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of i…
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection
Sudarshan Regmi, Bibek Panthi, Sakar Dotel +3
Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization ha…
Semi-Supervised Learning for Eye Image Segmentation
Aayush K. Chaudhary, Prashnna K. Gyawali, Linwei Wang +1
Recent advances in appearance-based models have shown improved eye tracking performance in difficult scenarios like occlusion due to eyelashes, eyelids or camera placement, and env…
Wavelets to the Rescue: Improving Sample Quality of Latent Variable Deep Generative Models
Prashnna K Gyawali, Rudra Saha, Linwei Wang +2
Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations. However,…