AIMC Topic: Deep Learning

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Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle.

Sensors (Basel, Switzerland)
Effective livestock management is critical for cattle farms in today's competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not fea...

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning.

Sensors (Basel, Switzerland)
Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be a...

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.

Genomics, proteomics & bioinformatics
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noises caused by s...

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

Nature biotechnology
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes int...

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach.

Sensors (Basel, Switzerland)
Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, the...

LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases.

Sensors (Basel, Switzerland)
This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The pro...

A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.

Scientific reports
Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are...

Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images.

Zeitschrift fur medizinische Physik
Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal ce...

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.

International journal of medical informatics
OBJECTIVE: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to pred...

A deep learning model for prognosis prediction after intracranial hemorrhage.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty....