AIMC Topic: Deep Learning

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Prediction of Soil Water-Soluble Organic Matter by Continuous Use of Corn Biochar Using Three-Dimensional Fluorescence Spectra and Deep Learning.

Computational intelligence and neuroscience
The purpose is to study the soil's water-soluble organic matter and improve the utilization rate of the soil layer. This exploration is based on the theories of three-dimensional fluorescence spectroscopy, deep learning, and biochar. Chernozem in Har...

Deep learning-based classification of adequate sonographic images for self-diagnosing deep vein thrombosis.

PloS one
BACKGROUND: Pulmonary thromboembolism is a serious disease that often occurs in disaster victims evacuated to shelters. Deep vein thrombosis is the most common reason for pulmonary thromboembolism, and early prevention is important. Medical technicia...

BCM-DTI: A fragment-oriented method for drug-target interaction prediction using deep learning.

Computational biology and chemistry
The identification of drug-target interaction (DTI) is significant in drug discovery and development, which is usually of high cost in time and money due to large amount of molecule and protein space. The application of deep learning in predicting DT...

Explainable deep learning-based clinical decision support engine for MRI-based automated diagnosis of temporomandibular joint anterior disk displacement.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: MRI is considered the gold standard for diagnosing anterior disc displacement (ADD), the most common temporomandibular joint (TMJ) disorder. However, even highly trained clinicians find it difficult to integrate the dynamic ...

Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task d...

An automated segmentation of coronary artery calcification using deep learning in specific region limitation.

Medical & biological engineering & computing
Coronary artery calcification (CAC) is a frequent disease of the arteries that supply the surface of the heart muscle. Leaving a severe disease untreated can make it permanent. Computer tomography (CT), which is well known for its ability to quantify...

A New Deep-Learning Method for Human Activity Recognition.

Sensors (Basel, Switzerland)
Currently, three-dimensional convolutional neural networks (3DCNNs) are a popular approach in the field of human activity recognition. However, due to the variety of methods used for human activity recognition, we propose a new deep-learning model in...

Automated identification of myocardial perfusion defects in dynamic cardiac computed tomography using deep learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The purpose of this study was to develop and evaluate deep convolutional neural network (CNN) models for quantifying myocardial blood flow (MBF) as well as for identifying myocardial perfusion defects in dynamic cardiac computed tomography (...

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast les...