AIMC Topic: Deep Learning

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Improved automatic detection of herpesvirus secondary envelopment stages in electron microscopy by augmenting training data with synthetic labelled images generated by a generative adversarial network.

Cellular microbiology
Detailed analysis of secondary envelopment of the herpesvirus human cytomegalovirus (HCMV) by transmission electron microscopy (TEM) is crucial for understanding the formation of infectious virions. Here, we present a convolutional neural network (CN...

If deep learning is the answer, what is the question?

Nature reviews. Neuroscience
Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep n...

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.

Sensors (Basel, Switzerland)
To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. How...

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era.

Scientific reports
Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis...

Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.

Nature communications
For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied...

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression.

Sensors (Basel, Switzerland)
To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To sol...

A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks.

International journal of environmental research and public health
Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object det...

Deep learning for species identification of bolete mushrooms with two-dimensional correlation spectral (2DCOS) images.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Bolete is well-known and widely consumed mushroom in the world. However, its medicinal properties and nutritional are completely different from one species to another. Therefore, the consumers need a fast and effective detection method to discriminat...

DeepSIBA: chemical structure-based inference of biological alterations using deep learning.

Molecular omics
Predicting whether a chemical structure leads to a desired or adverse biological effect can have a significant impact for in silico drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs ...

Thyroid nodules risk stratification through deep learning based on ultrasound images.

Medical physics
PURPOSE: Clinically, the risk stratification of thyroid nodules is usually used to formulate the next treatment plan. The American College of Radiology (ACR) thyroid imaging reporting and data system (TI-RADS) is a type of medical standard widely use...