5 citations · 5 across the 8 of their papers we have counts for
8 papers
A Unified Model for Highly Accurate ECG-Free Dynamic Coronary Roadmapping Using Spatio-Temporal Transformers
Saahil Islam, Sebastian Piat, Venkatesh N. Murthy +4
Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure used to restore coronary blood flow obstructed by atherosclerotic plaque. During PCI, repeated injections…
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…
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…
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features
Saahil Islam, Venkatesh N. Murthy, Dominik Neumann +5
To restore proper blood flow in blocked coronary arteries via angioplasty procedure, accurate placement of devices such as catheters, balloons, and stents under live fluoroscopy or…
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…
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…