Search Results for author:
Found 27 papers, 13 papers with code
HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction
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.
Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors
We propose a graph neural network-based transformer based on the Nodeformer architecture to measure and classify the segmentation errors at any point.
SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction
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.
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research.
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks
The hypernetworks and the reconstruction network in the GBML setting provide discriminative mode-specific features and low-level image features, respectively.
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning
Conventional deep learning methods deal with removing a specific type of artifact, leading to separately trained models for each artifact type that lack the shared knowledge generalizable across artifacts.
SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction
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.
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI
Anomaly detection in MRI is of high clinical value in imaging and diagnosis.
Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit
Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health.
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction
In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction.
MIST GAN: Modality Imputation Using Style Transfer for MRI
MRI entails a great amount of cost, time and effort for the generation of all the modalities that are recommended for efficient diagnosis and treatment planning.
MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction
We propose a multiple acquisition context based network, called MAC-ReconNet for MRI reconstruction, flexible to multiple acquisition contexts and generalizable to unseen contexts for applicability in real scenarios.
Style Transfer based Coronary Artery Segmentation in X-ray Angiogram
A deep learning-based edge adaptive instance normalization style transfer technique for segmenting the coronary arteries, is presented in this paper.
Early Detection of Retinopathy of Prematurity stage using Deep Learning approach
The system was tested on 45 images and reached detection accuracy of 0. 88, showing that deep learning detection with pre-processing by image normalization allows robust detection of ROP in early stages.
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and herniations.
Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks
One of the important parameters for the assessment of glaucoma is optic nerve head (ONH) evaluation, which usually involves depth estimation and subsequent optic disc and cup boundary extraction.
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow
In our work, we propose a knowledge distillation (KD) framework for the image to image problems in the MRI workflow in order to develop compact, low-parameter models without a significant drop in performance.
Detection and skeletonization of single neurons and tracer injections using topological methods
Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory.
A context based deep learning approach for unbalanced medical image segmentation
Foreground-background class imbalance is a common occurrence in medical images, and U-Net has difficulty in handling class imbalance because of its cross entropy (CE) objective function.
DC-WCNN: A deep cascade of wavelet based convolutional neural networks for MR Image Reconstruction
Among them, U-Net has shown to be the baseline architecture for MR image reconstruction.
Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction
Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance.
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation
For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications.
Deep Network for Capacitive ECG Denoising
We further evaluate the signal denoising using Mean Square Error(MSE) and Cross Correlation between model predictions and ground truth.
Ranked #1 on
ECG Denoising
on UnoViS_auto2012
RespNet: A deep learning model for extraction of respiration from photoplethysmogram
Recently, due to the widespread adoption of wearable smartwatches with in-built Photoplethysmogram (PPG) sensor, it is being considered as a viable candidate for continuous and unobtrusive respiration monitoring.
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
We also propose a new joint loss function for the proposed architecture.
Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH).
Joint shape learning and segmentation for medical images using a minimalistic deep network
We modify the decoder part of the FCN to exploit class information and the structural information as well.
