AIMC Topic: Deep Learning

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Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Detecting blood content in the gastrointestinal tract is one of the crucial applications of capsule endoscopy (CE). The suspected blood indicator (SBI) is a conventional tool used to automatically tag images depicting possible ble...

Family history information extraction via deep joint learning.

BMC medical informatics and decision making
Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making proce...

DeepMF: deciphering the latent patterns in omics profiles with a deep learning method.

BMC bioinformatics
BACKGROUND: With recent advances in high-throughput technologies, matrix factorization techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering ins...

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

Physiological measurement
OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as ...

Automated detection & classification of knee arthroplasty using deep learning.

The Knee
BACKGROUND: Preoperative identification of knee arthroplasty is important for planning revision surgery. However, up to 10% of implants are not identified prior to surgery. The purposes of this study were to develop and test the performance of a deep...

DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation.

Journal of healthcare engineering
Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries ar...

A novel model for malaria prediction based on ensemble algorithms.

PloS one
BACKGROUND AND OBJECTIVE: Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can s...

A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, ...

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

Medical physics
PURPOSE: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the ac...

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.

Academic radiology
Recent advances in fetal imaging open the door to enhanced detection of fetal disorders and computer-assisted surgical planning. However, precise segmentation of womb's tissues is challenging due to motion artifacts caused by fetal movements and mate...