2 citations · 2 across the 3 of their papers we have counts for
3 papers
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
Bidur Khanal, Prashant Shrestha, Sanskar Amgain +3
Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability. The test performanc…
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…
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…