AIMC Topic: Deep Learning

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Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH.

Cell reports. Medicine
Nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease globally and a leading cause for liver transplantation in the US. Its pathogenesis remains imprecisely defined. We combined two high-resolution modalities to tissue samples ...

Yield prediction through integration of genetic, environment, and management data through deep learning.

G3 (Bethesda, Md.)
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past dec...

Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture.

Mathematical biosciences and engineering : MBE
One of the most effective approaches for identifying breast cancer is histology, which is the meticulous inspection of tissues under a microscope. The kind of cancer cells, or whether they are cancerous (malignant) or non-cancerous, is typically dete...

Modulating hierarchical learning by high-definition transcranial alternating current stimulation at theta frequency.

Cerebral cortex (New York, N.Y. : 1991)
Considerable evidence highlights the dorsolateral prefrontal cortex (DLPFC) as a key region for hierarchical (i.e. multilevel) learning. In a previous electroencephalography (EEG) study, we found that the low-level prediction errors were encoded by f...

Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set.

Translational vision science & technology
PURPOSE: To diagnose and segment choroidal neovascularization (CNV) in a real-world multicenter clinical OCT angiography (OCTA) data set using deep learning.

MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.

Bioinformatics (Oxford, England)
MOTIVATION: Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell li...

A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.

Bioinformatics (Oxford, England)
MOTIVATION: Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundament...

Effective and efficient active learning for deep learning-based tissue image analysis.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive ...

OPTIMIZATION OF PRE-HOSPITAL FIRST AID MANAGEMENT STRATEGIES FOR PATIENTS WITH INFECTIOUS DISEASES IN HUIZHOU CITY USING DEEP LEARNING ALGORITHM.

Acta clinica Croatica
The aim of the study was to optimize the pre-hospital first aid management strategy for patients with infectious diseases in Huizhou city, which is expected to provide a basis for the epidemic prevention and control, to save lives, and increase the p...

[Application of Deep Learning in Differential Diagnosis of Ameloblastoma and Odontogenic Keratocyst Based on Panoramic Radiographs].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between m...