AIMC Topic: Deep Learning

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Converting tabular data into images for deep learning with convolutional neural networks.

Scientific reports
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relation...

An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning.

Computational and mathematical methods in medicine
Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than ha...

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.

Journal of healthcare engineering
In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnos...

Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well ...

LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.

International journal of computer assisted radiology and surgery
PURPOSE: Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the manual examination of histopathology images. Due to its laborious nature, we focus on alternate dee...

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images.

Biomolecules
It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and trea...

The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients.

Artificial intelligence in medicine
Critical care clinicians are trained to analyze simultaneously multiple physiological parameters to predict critical conditions such as hemodynamic instability. We developed the Multi-task Learning Physiological Deep Learner (MTL-PDL), a deep learnin...

A survey on applications of deep learning in microscopy image analysis.

Computers in biology and medicine
Advanced microscopy enables us to acquire quantities of time-lapse images to visualize the dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in signal-to-noise ratios and include a wealth of information which re...

A robust real-time deep learning based automatic polyp detection system.

Computers in biology and medicine
Colorectal cancer (CRC) is globally the third most common type of cancer. Colonoscopy is considered the gold standard in colorectal cancer screening and allows for the removal of polyps before they become cancerous. Computer-aided detection systems (...