8 citations · 11 across the 11 of their papers we have counts for
11 papers
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…
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…
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images
Bipasha Kundu, Bidur Khanal, Richard Simon +1
Accurate left atrium (LA) segmentation from pre-operative scans is crucial for diagnosing atrial fibrillation, treatment planning, and supporting surgical interventions. While deep…
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…
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…
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…
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining
Bidur Khanal, Binod Bhattarai, Bishesh Khanal +1
Noisy labels hurt deep learning-based supervised image classification performance as the models may overfit the noise and learn corrupted feature extractors. For natural image clas…
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
Bidur Khanal, Binod Bhattarai, Bishesh Khanal +2
Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize…
How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?
Bidur Khanal, Christopher Kanan
Incorrectly labeled examples, or label noise, is common in real-world computer vision datasets. While the impact of label noise on learning in deep neural networks has been studied…
Label Geometry Aware Discriminator for Conditional Generative Networks
Suman Sapkota, Bidur Khanal, Binod Bhattarai +2
Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these sy…
Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression
Bidur Khanal, Lavsen Dahal, Prashant Adhikari +1
Correct evaluation and treatment of Scoliosis require accurate estimation of spinal curvature. Current gold standard is to manually estimate Cobb Angles in spinal X-ray images whic…