AIMC Topic: Deep Learning

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Predicting potential residues associated with lung cancer using deep neural network.

Mutation research
Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) occur mainly due to somatic driver mutations in proteins and screening of su...

A review on medical imaging synthesis using deep learning and its clinical applications.

Journal of applied clinical medical physics
This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing...

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records.

Sensors (Basel, Switzerland)
The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and reporting diseases, injuries, disorders, and health conditions. Most previously-proposed disease predic...

Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning.

Sensors (Basel, Switzerland)
A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in senti...

Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization.

Sensors (Basel, Switzerland)
White blood cells (WBCs) are essential components of the immune system in the human body. Various invasive and noninvasive methods to monitor the condition of the WBCs have been developed. Among them, a noninvasive method exploits an optical characte...

Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning.

Scientific reports
In aneurysmal subarachnoid hemorrhage (aSAH), accurate diagnosis of aneurysm is essential for subsequent treatment to prevent rebleeding. However, aneurysm detection proves to be challenging and time-consuming. The purpose of this study was to develo...

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Nature communications
Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be system...

Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies.

BMC gastroenterology
BACKGROUND: Helicobacter pylori, a 2 × 1 μm spiral-shaped bacterium, is the most common risk factor for gastric cancer worldwide. Clinically, patients presenting with symptoms of gastritis, routinely undergo gastric biopsies. The following histo-morp...

A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT.

Physics in medicine and biology
Helical CT has been widely used in clinical diagnosis. In this work, we focus on a new prototype of helical CT, equipped with sparsely spaced multidetector and multi-slit collimator (MSC) in the axis direction. This type of system can not only lower ...

Deep Learning CT Image Reconstruction in Clinical Practice.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
BACKGROUND:  Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomica...