4 papers
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…
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…
SegResMamba: An Efficient Architecture for 3D Medical Image Segmentation
Badhan Kumar Das, Ajay Singh, Saahil Islam +2
The Transformer architecture has opened a new paradigm in the domain of deep learning with its ability to model long-range dependencies and capture global context and has outpaced…
Self-Supervised Learning for Interventional Image Analytics: Towards Robust Device Trackers
Saahil Islam, Venkatesh N. Murthy, Dominik Neumann +5
An accurate detection and tracking of devices such as guiding catheters in live X-ray image acquisitions is an essential prerequisite for endovascular cardiac interventions. This i…