Search Results for author: Mohammad Al Fahim

Found 4 papers, 3 papers with code

Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors

no code implementations9 Aug 2023 Sneha Sree C, Mohammad Al Fahim, Keerthi Ram, Mohanasankar Sivaprakasam

We propose a graph neural network-based transformer based on the Nodeformer architecture to measure and classify the segmentation errors at any point.

Image Segmentation Segmentation +1

HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction

1 code implementation9 Aug 2023 Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G. S., Amrit Kumar Jethi, Keerthi Ram, Mohanasankar Sivaprakasam

Experiments reveal that our approach 1) adapts on the fly to various unseen configurations up to 32 coils when trained on lower numbers (i. e. 7 to 11) of randomly varying coils, and to 120 deviated unseen configurations when trained on 18 configurations in a single model, 2) matches the performance of coil configuration-specific models, and 3) outperforms configuration-invariant models with improvement margins of around 1 dB / 0. 03 and 0. 3 dB / 0. 02 in PSNR / SSIM for knee and brain data.

Anatomy MRI Reconstruction +1

SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction

1 code implementation8 Aug 2023 Rahul G. S., Sriprabha Ramnarayanan, Mohammad Al Fahim, Keerthi Ram, Preejith S. P, Mohanasankar Sivaprakasam

The self-attention mechanism of the transformer enables transformers to capture long-range dependencies in the images, which might be desirable for accelerated MRI image reconstruction as the effect of undersampling is non-local in the image domain.

Computational Efficiency Image Reconstruction +2

SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction

1 code implementation11 Apr 2023 Matcha Naga Gayathri, Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G S, Keerthi Ram, Mohanasankar Sivaprakasam

We propose SFT-KD-Recon, a student-friendly teacher training approach along with the student as a prior step to KD to make the teacher aware of the structure and capacity of the student and enable aligning the representations of the teacher with the student.

Image Reconstruction Knowledge Distillation

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