This demonstrates the viability of KD for performance improvement of single-channel ECG based sleep staging in 4-class(W-L-D-R) and 3-class(W-N-R) classification.
The multitasking network consists of a combination of Encoder-Decoder and Encoder-IncResNet, to fetch the average respiration rate and the respiration signal.
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.
1 code implementation • 10 Nov 2020 • Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos, Nikita Moriakov, Mattha Caan, George Yiasemis, Lívia Rodrigues, Alexandre Lopes, Hélio Pedrini, Letícia Rittner, Maik Dannecker, Viktor Studenyak, Fabian Gröger, Devendra Vyas, Shahrooz Faghih-Roohi, Amrit Kumar Jethi, Jaya Chandra Raju, Mohanasankar Sivaprakasam, Mike Lasby, Nikita Nogovitsyn, Wallace Loos, Richard Frayne, Roberto Souza
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.
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.
2 code implementations • 24 Jan 2020 • Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicolás Pérez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Tao Jiang, Xinjun Ma, Christoph Angerman, Xin Wang, Kevin Brown, Alexandre Kirszenberg, Élodie Puybareau, Di Chen, Yiwei Bai, Brandon H. Rapazzo, Timyoas Yeah, Amber Zhang, Shangliang Xu, Feng Hou, Zhiqiang He, Chan Zeng, Zheng Xiangshang, Xu Liming, Tucker J. Netherton, Raymond P. Mumme, Laurence E. Court, Zixun Huang, Chenhang He, Li-Wen Wang, Sai Ho Ling, Lê Duy Huynh, Nicolas Boutry, Roman Jakubicek, Jiri Chmelik, Supriti Mulay, Mohanasankar Sivaprakasam, Johannes C. Paetzold, Suprosanna Shit, Ivan Ezhov, Benedikt Wiestler, Ben Glocker, Alexander Valentinitsch, Markus Rempfler, Björn H. Menze, Jan S. Kirschke
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
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
Traditional machine learning and deep learning approaches rely on tri-axial accelerometer data along with PPG to perform heart rate estimation.
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.