AIMC Topic: Deep Learning

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A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification.

Sensors (Basel, Switzerland)
Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated...

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm.

Genes
One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequen...

Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature.

Sensors (Basel, Switzerland)
Human following is one of the fundamental functions in human-robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well...

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets.

IEEE transactions on medical imaging
Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a system...

Deep learning enables automated localization of the metastatic lymph node for thyroid cancer on I post-ablation whole-body planar scans.

Scientific reports
The accurate detection of radioactive iodine-avid lymph node (LN) metastasis on I post-ablation whole-body planar scans (RxWBSs) is important in tracking the progression of the metastatic lymph nodes (mLNs) of patients with papillary thyroid cancer (...

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning.

Computational and mathematical methods in medicine
Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consum...

Predicting cancer origins with a DNA methylation-based deep neural network model.

PloS one
Cancer origin determination combined with site-specific treatment of metastatic cancer patients is critical to improve patient outcomes. Existing pathology and gene expression-based techniques often have limited performance. In this study, we develop...

Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video).

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Helicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even after the successful eradication of H. pylori, an increased, i.e., moderate, risk of gastric cancer pe...

Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients.

Magnetic resonance in medicine
PURPOSE: Quantification of myocardial perfusion has the potential to improve the detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF)...

Deep learning from label proportions with labeled samples.

Neural networks : the official journal of the International Neural Network Society
Learning from label proportions (LLP), where the training data is in form of bags, and only the proportions of classes in each bag are available, has attracted wide interest in machine learning community. In general, most LLP algorithms adopt random ...