29 citations · 91 across the 48 of their papers we have counts for
50 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…
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…
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…
BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation
Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali +2
High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.g., omics) are characterized by , where = number of samples, and = number of features. Such domains oft…
GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Kumar Gyawali +2
We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones. We introdu…
Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
Shourov Joarder, Diganta Sikdar, Ahsan Habib Akash +2
Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of LLMs, but often depends on external supervision from human annotations or…
Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis
Jacob Thrasher, Kaitlyn Heintzelman, Peter Martone +4
Alzheimer's Dementia (AD) is a progressive neurodegenerative disease marked by irreversible decline, making reliable modeling of its progression essential for effective patient car…
FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
Alina Devkota, Jacob Thrasher, Donald Adjeroh +2
Federated Learning (FL) enables collaborative model training across multiple clients without sharing their private data. However, data heterogeneity across clients leads to client…
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,…
Local K-Similarity Constraint for Federated Learning with Label Noise
Sanskar Amgain, Prashant Shrestha, Bidur Khanal +5
Federated learning on clients with noisy labels is a challenging problem, as such clients can infiltrate the global model, impacting the overall generalizability of the system. Exi…
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…
Multimodal Federated Learning With Missing Modalities through Feature Imputation Network
Pranav Poudel, Aavash Chhetri, Prashnna Gyawali +2
Multimodal federated learning holds immense potential for collaboratively training models from multiple sources without sharing raw data, addressing both data scarcity and privacy…
Supervised Pretraining for Material Property Prediction
Chowdhury Mohammad Abid Rahman, Aldo H. Romero, Prashnna K. Gyawali
Accurate prediction of material properties facilitates the discovery of novel materials with tailored functionalities. Deep learning models have recently shown superior accuracy an…
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…
Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks
Yusuf Usman, Aadesh Upadhyay, Prashnna Gyawali +1
In an era where digital threats are increasingly sophisticated, the intersection of Artificial Intelligence and cybersecurity presents both promising defenses and potent dangers. T…
Enhancing material property prediction with ensemble deep graph convolutional networks
Chowdhury Mohammad Abid Rahman, Ghadendra Bhandari, Nasser M Nasrabadi +2
Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and st…
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…