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.
A deep learning-based edge adaptive instance normalization style transfer technique for segmenting the coronary arteries, is presented in this paper.
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.
Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and herniations.
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.
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.
Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory.
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.
Among them, U-Net has shown to be the baseline architecture for MR image reconstruction.
Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance.
For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications.
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
no code implementations • 12 Feb 2019 • Vignesh Ravichandran, Balamurali Murugesan, Vaishali Balakarthikeyan, Sharath M. Shankaranarayana, Keerthi Ram, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam
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.
We also propose a new joint loss function for the proposed architecture.
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH).
We modify the decoder part of the FCN to exploit class information and the structural information as well.