AIMC Topic: Deep Learning

Clear Filters Showing 19521 to 19530 of 28423 articles

Greedy auto-augmentation for n-shot learning using deep neural networks.

Neural networks : the official journal of the International Neural Network Society
The goal of n-shot learning is the classification of input data from small datasets. This type of learning is challenging in neural networks, which typically need a high number of data during the training process. Recent advancements in data augmenta...

Quantitative analysis of brain herniation from non-contrast CT images using deep learning.

Journal of neuroscience methods
BACKGROUND: Brain herniation is one of the fatal outcomes of increased intracranial pressure (ICP). It is caused due to the presence of hematoma or tumor mass in the brain. Ideal midline (iML) divides the healthy brain into two (right and left) nearl...

Detection of Snore from OSAHS Patients Based on Deep Learning.

Journal of healthcare engineering
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively ...

Non-destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning.

Journal of the science of food and agriculture
BACKGROUND: This paper proposes a novel method to improve accuracy and efficiency in detecting the quality of blueberry fruit, taking advantage of deep learning in classification tasks. We first collected 'Tifblue' blueberries at seven different stag...

3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Classifying ground-glass lung nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) on diagnostic CT images is important to evaluate the th...

Deep learning powers cancer diagnosis in digital pathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Technological innovation has accelerated the pathological diagnostic process for cancer, especially in digitizing histopathology slides and incorporating deep learning-based approaches to mine the subvisual morphometric phenotypes for improving patho...

Deep multi-kernel auto-encoder network for clustering brain functional connectivity data.

Neural networks : the official journal of the International Neural Network Society
In this study, we propose a deep-learning network model called the deep multi-kernel auto-encoder clustering network (DMACN) for clustering functional connectivity data for brain diseases. This model is an end-to-end clustering algorithm that can lea...

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status.

Computers in biology and medicine
Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtai...

Quantitative analysis of excipient dominated drug formulations by Raman spectroscopy combined with deep learning.

Analytical methods : advancing methods and applications
Owing to the growing interest in the application of Raman spectroscopy for quantitative purposes in solid pharmaceutical preparations, an article on the identification of compositions in excipient dominated drugs based on Raman spectra is presented. ...