AIMC Topic: Deep Learning

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FBSED based automatic diagnosis of COVID-19 using X-ray and CT images.

Computers in biology and medicine
This work introduces the Fourier-Bessel series expansion-based decomposition (FBSED) method, which is an implementation of the wavelet packet decomposition approach in the Fourier-Bessel series expansion domain. The proposed method has been used for ...

Using deep neural networks for kinematic analysis: Challenges and opportunities.

Journal of biomechanics
Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such analyses without markers, ma...

Automated assessment of glomerulosclerosis and tubular atrophy using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance...

Interpreting deep learning models for epileptic seizure detection on EEG signals.

Artificial intelligence in medicine
While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-ligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability ...

LBP-based information assisted intelligent system for COVID-19 identification.

Computers in biology and medicine
A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In ...

"Keep it simple, scholar": an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging.

International journal of computer assisted radiology and surgery
PURPOSE: With the recent development of deep learning technologies, various neural networks have been proposed for fundus retinal vessel segmentation. Among them, the U-Net is regarded as one of the most successful architectures. In this work, we sta...

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality.

DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning.

IEEE transactions on medical imaging
Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial res...

Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition.

IEEE transactions on medical imaging
The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in ...

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

IEEE transactions on medical imaging
Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually a...