AIMC Topic: Humans

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Optimal number of strong labels for curriculum learning with convolutional neural network to classify pulmonary abnormalities in chest radiographs.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: It is important to alleviate annotation efforts and costs by efficiently training on medical images. We performed a stress test on several strong labels for curriculum learning with a convolutional neural network to differen...

Attention-based deep learning system for automated diagnoses of age-related macular degeneration in optical coherence tomography images.

Medical physics
PURPOSE: The progression of age-related macular degeneration (AMD) is critical to treatment decisions in clinical practice. The disease can be classified into four categories namely, drusen, inactive choroidal neovascularization (CNV), active CNV, an...

A Flexible Multimodal Sole Sensor for Legged Robot Sensing Complex Ground Information during Locomotion.

Sensors (Basel, Switzerland)
Recent achievements in the field of computer vision, reinforcement learning, and locomotion control have largely extended legged robots' maneuverability in complex natural environments. However, little research focuses on sensing and analyzing the ph...

Machine Learning Approach for Active Vaccine Safety Monitoring.

Journal of Korean medical science
BACKGROUND: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active...

A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drug...

Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images.

Scientific reports
Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making early detection through regular checkups vitally immportant. In this stu...

Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection.

Scientific reports
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 ...

Validating deep learning inference during chest X-ray classification for COVID-19 screening.

Scientific reports
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the...

Toward a systematic conflict resolution framework for ontologies.

Journal of biomedical semantics
BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to ...

Chronic kidney disease diagnosis using decision tree algorithms.

BMC nephrology
BACKGROUND: Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severit...