AIMC Topic: Neural Networks, Computer

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Automated detection of cribriform growth patterns in prostate histology images.

Scientific reports
Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth pa...

Accelerated training of bootstrap aggregation-based deep information extraction systems from cancer pathology reports.

Journal of biomedical informatics
OBJECTIVE: In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources an...

British Sign Language Recognition via Late Fusion of Computer Vision and Leap Motion with Transfer Learning to American Sign Language.

Sensors (Basel, Switzerland)
In this work, we show that a late fusion approach to multimodality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of image classification (88.14%) and Leap Motion data classification (7...

Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest.

Sensors (Basel, Switzerland)
As the demand for human-friendly computing increases, research on pupil tracking to facilitate human-machine interactions (HCIs) is being actively conducted. Several successful pupil tracking approaches have been developed using images and a deep neu...

A Tactile Method for Rice Plant Recognition Based on Machine Learning.

Sensors (Basel, Switzerland)
Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice l...

Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection.

mSphere
The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed "mimicry embedding," for...

Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend.

Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice.

Computational intelligence and neuroscience
Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. D...

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screeni...