most citedEnhancing material property prediction with ensemble deep graph convolutional networks

2 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20242 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2023

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…