AIMC Topic: Neural Networks, Computer

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Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Psychological assessment
Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the ...

Recurrent neural networks with segment attention and entity description for relation extraction from clinical texts.

Artificial intelligence in medicine
At present, great progress has been achieved on the relation extraction for clinical texts, but we have noticed that the current models have great drawbacks when dealing with long sentences and multiple entities in a sentence. In this paper, we propo...

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

The Lancet. Digital health
BACKGROUND: Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a po...

Emotion classification using a CNN_LSTM-based model for smooth emotional synchronization of the humanoid robot REN-XIN.

PloS one
In this paper, we propose an Emotional Trigger System to impart an automatic emotion expression ability within the humanoid robot REN-XIN, in which the Emotional Trigger is an emotion classification model trained from our proposed Word Mover's Distan...

Potential Role of Machine Learning in Oncology.

The journal of contemporary dental practice
Machine learning (ML) is the ability of computers to learn from data autonomously. It is a core branch of artificial intelligence (AI), which is defined as the ability of a machine to replicate the intellectual processes of humans independently. The ...

Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction.

Scientific reports
In recent years, some convolutional neural networks (CNNs) have been proposed to segment sub-cortical brain structures from magnetic resonance images (MRIs). Although these methods provide accurate segmentation, there is a reproducibility issue regar...

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

Scientific reports
Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart's activity. However, automated medical-aided diagnosis with computers usually requires a large volume of...

Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks.

PloS one
Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity. Recently, deep learning based methods have...

Computer-assisted prediction of atherosclerotic intimal thickness based on weight of adrenal gland, interleukin-6 concentration, and neural networks.

The Journal of international medical research
OBJECTIVE: Atherosclerosis (AS) is the main pathological basis of ischemic cardio-cerebrovascular diseases, and the intimal thickness (IT) of large arteries is regarded as a powerful evaluation indicator for AS. We established an effective neural net...

Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach.