AIMC Topic: Deep Learning

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Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach.

BMC oral health
BACKGROUND: Oral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combin...

Predicting Infectious Disease Using Deep Learning and Big Data.

International journal of environmental research and public health
Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to mi...

Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?

International journal of neural systems
Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging moda...

Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks.

Annals of biomedical engineering
The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor ...

Complex-valued unsupervised convolutional neural networks for sleep stage classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be...

Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.

Journal of dentistry
OBJECTIVES: Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to e...

Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Convolutional neural networks are a powerful technology for image recognition. This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and suba...

Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia.

NeuroImage
This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consi...

The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents' Exposure to Urban Greenness.

International journal of environmental research and public health
Many studies have established that urban greenness is associated with better health outcomes. Yet most studies assess urban greenness with overhead-view measures, such as park area or tree count, which often differs from the amount of greenness perce...