5 citations · 10 across the 12 of their papers we have counts for
4 papers · 2 filters
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation
Badhan Kumar Das, Ajay Singh, Gengyan Zhao +5
Self-supervised pretrain techniques have been widely used to improve the downstream tasks' performance. However, real-world magnetic resonance (MR) studies usually consist of diffe…
Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Data
Badhan Kumar Das, Gengyan Zhao, Boris Mailhe +4
Identifying brain hemorrhages from magnetic resonance imaging (MRI) is a critical task for healthcare professionals. The diverse nature of MRI acquisitions with varying contrasts a…
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities
Badhan Kumar Das, Gengyan Zhao, Han Liu +4
Pretrain techniques, whether supervised or self-supervised, are widely used in deep learning to enhance model performance. In real-world clinical scenarios, different sets of magne…
Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast 3D Medical Imaging
Badhan Kumar Das, Gengyan Zhao, Han Liu +4
The Masked Autoencoder (MAE) has recently demonstrated effectiveness in pre-training Vision Transformers (ViT) for analyzing natural images. By reconstructing complete images from…