AIMC Topic: Deep Learning

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Using deep learning to associate human genes with age-related diseases.

Bioinformatics (Oxford, England)
MOTIVATION: One way to identify genes possibly associated with ageing is to build a classification model (from the machine learning field) capable of classifying genes as associated with multiple age-related diseases. To build this model, we use a pr...

ImPLoc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images.

Bioinformatics (Oxford, England)
MOTIVATION: The tissue atlas of the human protein atlas (HPA) houses immunohistochemistry (IHC) images visualizing the protein distribution from the tissue level down to the cell level, which provide an important resource to study human spatial prote...

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.

Journal of digital imaging
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural net...

Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography.

European heart journal. Cardiovascular Imaging
AIMS: Although deep-learning algorithms have been used to compute fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA), no study has achieved 'fully automated' (i.e. free from human input) FFR calculation using deep-lear...

[Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives].

The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi
Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution of computation po...

Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...

Artificial Intelligence and Ophthalmology.

Turkish journal of ophthalmology
Artificial intelligence is advancing rapidly and making its way into all areas of our lives. This review discusses developments and potential practices regarding the use of artificial intelligence in the field of ophthalmology, and the related topic ...

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

Neuro-oncology
BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a high...

Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures.

Lab on a chip
Uncovering the heterogeneity of cellular populations and multicellular constructs is a long-standing goal in fields ranging from antimicrobial resistance to cancer research. Emerging technology platforms such as droplet microfluidics hold the promise...

Application of Convolutional Neural Networks for Detection of Superficial Nonampullary Duodenal Epithelial Tumors in Esophagogastroduodenoscopic Images.

Clinical and translational gastroenterology
OBJECTIVES: A superficial nonampullary duodenal epithelial tumor (SNADET) is defined as a mucosal or submucosal sporadic tumor of the duodenum that does not arise from the papilla of Vater. SNADETs rarely metastasize to the lymph nodes, and most can ...