8 citations · 19 across the 7 of their papers we have counts for
7 papers
VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks
Manish Dhakal, Rabin Adhikari, Safal Thapaliya +1
Foundation Vision-Language Models (VLMs) trained using large-scale open-domain images and text pairs have recently been adapted to develop Vision-Language Segmentation Models (VLSM…
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…
Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction
Mahesh Shakya, Bishesh Khanal
Various deep learning models have been proposed for 3D bone shape reconstruction from two orthogonal (biplanar) X-ray images. However, it is unclear how these models compare agains…
Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in Echocardiography
Rabin Adhikari, Manish Dhakal, Safal Thapaliya +3
Accurate segmentation is essential for echocardiography-based assessment of cardiovascular diseases (CVDs). However, the variability among sonographers and the inherent challenges…
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…