AIMC Topic: Deep Learning

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Deep contextualized embeddings for quantifying the informative content in biomedical text summarization.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Capturing the context of text is a challenging task in biomedical text summarization. The objective of this research is to show how contextualized embeddings produced by a deep bidirectional language model can be utilized to...

Progressive Sub-Band Residual-Learning Network for MR Image Super Resolution.

IEEE journal of biomedical and health informatics
High-resolution (HR) magnetic resonance images (MRI) provide more detailed information for clinical application. However, HR MRI is less available because of the longer scan time and lower signal-to-noise ratio. Spatial resolution is one of the key p...

ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning.

Scientific reports
Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely a...

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Medical image analysis
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid ...

A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices.

Journal of healthcare engineering
Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, each individual now expects to be delivered with state-of-the-art reports about his body. The most dom...

Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
BACKGROUND AND AIM: To examine whether our convolutional neural network (CNN) system based on deep learning can reduce the reading time of endoscopists without oversight of abnormalities in the capsule-endoscopy reading process.

Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
BACKGROUND AND AIM: Although small-bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule endoscopy (CE) in patients with obscure gastrointestinal bleeding, a computer-aided detection metho...

Simulation of hyperelastic materials in real-time using deep learning.

Medical image analysis
The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel ...

Extracting comprehensive clinical information for breast cancer using deep learning methods.

International journal of medical informatics
OBJECTIVE: Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant in multiple types of clinical fields, including clinicopathological data, genotype and phenotype in...

Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.

JAMA network open
IMPORTANCE: A high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and ...