AIMC Topic: Neural Networks, Computer

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MFMSNet: A Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network for breast ultrasound image segmentation.

Computers in biology and medicine
Breast tumor segmentation in ultrasound images is fundamental for quantitative analysis and plays a crucial role in the diagnosis and treatment of breast cancer. Recently, existing methods have mainly focused on spatial domain implementations, with l...

Application of convolutional neural network for differentiating ovarian thecoma-fibroma and solid ovarian cancer based on MRI.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Ovarian thecoma-fibroma and solid ovarian cancer have similar clinical and imaging features, and it is difficult for radiologists to differentiate them. Since the treatment and prognosis of them are different, accurate characterization is...

Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Journal of chemical information and modeling
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand ...

Workout Classification Using a Convolutional Neural Network in Ensemble Learning.

Sensors (Basel, Switzerland)
To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizin...

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

BMC medical imaging
Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. ...

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

Scientific reports
We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to ...

Vision-aided grasp classification: design and evaluation of compact CNN for prosthetic hands.

Biomedical physics & engineering express
Powered prosthetic hands capable of executing various grasp patterns are highly sought-after solutions for upper limb amputees. A crucial requirement for such prosthetic hands is the accurate identification of the intended grasp pattern and subsequen...

Artificial neural networks for model identification and parameter estimation in computational cognitive models.

PLoS computational biology
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to id...

Using neural ordinary differential equations to predict complex ecological dynamics from population density data.

Journal of the Royal Society, Interface
Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modelling bias due to simplifying assumptions or unaccounted factors, limiting their predic...

Specific emitter identification based on multiple sequence feature learning.

PloS one
The specific emitter identification is widely used in electronic countermeasures, spectrum control, wireless network security and other civil and military fields. In response to the problems that the traditional specific emitter identification algori...