5 citations · 10 across the 12 of their papers we have counts for
12 papers
MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI
Athira J. Jacob, Puneet Sharma, Dorin Comaniciu +1
Cardiac magnetic resonance imaging (CMR) produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep learning approaches process indivi…
Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization
Isabella Poles, Simon Arberet, Riqiang Gao +5
Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue sparing. Yet, its planning solv…
Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
Yuhan Wang, Zihan Li, Han Liu +7
Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often struggle to generalize across di…
EchoVLM: Measurement-Grounded Multimodal Learning for Echocardiography
Yuheng Li, Yue Zhang, Abdoul Aziz Amadou +5
Echocardiography is the most widely used imaging modality in cardiology, yet its interpretation remains labor-intensive and inherently multimodal, requiring view recognition, quant…
Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification
Han Liu, Bogdan Georgescu, Yanbo Zhang +8
3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for scaling to new tasks, yet curre…
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…
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…
Towards a vision foundation model for comprehensive assessment of Cardiac MRI
Athira J Jacob, Indraneel Borgohain, Teodora Chitiboi +3
Cardiac magnetic resonance imaging (CMR), considered the gold standard for noninvasive cardiac assessment, is a diverse and complex modality requiring a wide variety of image proce…
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…