AIMC Topic: Humans

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Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models.

Annals of biomedical engineering
Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a ma...

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment.

Radiology
Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and ...

A deep learning-based system for assessment of serum quality using sample images.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Serum quality is an important factor in the pre-analytical phase of laboratory analysis. Visual inspection of serum quality (including recognition of hemolysis, icterus, and lipemia) is widely used in clinical laboratories but is time-con...

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries.

Advanced materials (Deerfield Beach, Fla.)
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of a...

A High-Fidelity Skin-Attachable Acoustic Sensor for Realizing Auditory Electronic Skin.

Advanced materials (Deerfield Beach, Fla.)
Wearable auditory sensors are critical in user-friendly sound-recognition systems for smart human-machine interaction and the Internet of Things. However, previously reported wearable sensors have limited sound-sensing quality as a consequence of a p...

A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture.

Sensors (Basel, Switzerland)
Recognizing various abnormal human activities from video is very challenging. This problem is also greatly influenced by the lack of datasets containing various abnormal human activities. The available datasets contain various human activities, but o...

A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.

Scientific reports
Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. Developing an automatically computer-aided algorithm for glioma subtype classification is challenging due to many factors. One of the diffi...

Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates.

Scientific reports
Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radio...

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms.

Computational intelligence and neuroscience
Millions of people worldwide suffer from depression. Assessing, treating, and preventing recurrence requires early detection of depressive symptoms as depression-related datasets expand and machine learning improves, intelligent approaches to detect ...

Generation of Human Micro-Doppler Signature Based on Layer-Reduced Deep Convolutional Generative Adversarial Network.

Computational intelligence and neuroscience
Human activity recognition (HAR) using radar micro-Doppler has attracted the attention of researchers in the last decade. Using radar for human activity recognition has been very practical because of its unique advantages. There are several classifie...