8 citations · 24 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Bidur Khanal, Tianhong Dai, Binod Bhattarai +1
The robustness of supervised deep learning-based medical image classification is significantly undermined by label noise. Although several methods have been proposed to enhance cla…
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…
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?
Bidur Khanal, Binod Bhattarai, Bishesh Khanal +1
Noisy labels can significantly impact medical image classification, particularly in deep learning, by corrupting learned features. Self-supervised pretraining, which doesn't rely o…