5 citations · 9 across the 4 of their papers we have counts for
4 papers
SHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction
Sriprabha Ramanarayanan, Rahul G. S., Mohammad Al Fahim +3
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between distant pixel neighborhoods to compute feature representa…
Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors
Sneha Sree C, Mohammad Al Fahim, Keerthi Ram +1
Many segmentation networks have been proposed for 3D volumetric segmentation of tumors and organs at risk. Hospitals and clinical institutions seek to accelerate and minimize the e…
SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction
Rahul G. S., Sriprabha Ramnarayanan, Mohammad Al Fahim +3
Transformers have emerged as viable alternatives to convolutional neural networks owing to their ability to learn non-local region relationships in the spatial domain. The self-att…
SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction
Matcha Naga Gayathri, Sriprabha Ramanarayanan, Mohammad Al Fahim +3
Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of casc…