AIMC Topic: Neural Networks, Computer

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Spatial oblivion channel attention targeting intra-class diversity feature learning.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) have successfully driven many visual recognition tasks including image classification. However, when dealing with classification tasks with intra-class sample style diversity, the network tends to be disturbed by ...

Improving domain generalization performance for medical image segmentation via random feature augmentation.

Methods (San Diego, Calif.)
Deep convolutional neural networks (DCNNs) have shown remarkable performance in medical image segmentation tasks. However, medical images frequently exhibit distribution discrepancies due to variations in scanner vendors, operators, and image quality...

Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges.

Artificial intelligence in medicine
Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient data. However, imbalanced datasets pose a major problem for the training process and hence data augmentation is commonly performed. Generative adversarial netw...

Deep Learning for Counting People from UWB Channel Impulse Response Signals.

Sensors (Basel, Switzerland)
The use of higher frequency bands compared to other wireless communication protocols enhances the capability of accurately determining locations from ultra-wideband (UWB) signals. It can also be used to estimate the number of people in a room based o...

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.

Sensors (Basel, Switzerland)
Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; t...

Efficient Photoacoustic Image Synthesis with Deep Learning.

Sensors (Basel, Switzerland)
Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging underlying quantification problem. While in silico studies have revealed the great pote...

CaMeL-Net: Centroid-aware metric learning for efficient multi-class cancer classification in pathology images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated...

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.

Medical & biological engineering & computing
Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies...

Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population.

Biochemical and biophysical research communications
Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification ...

Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet.

Neural networks : the official journal of the International Neural Network Society
We predict steady-state Stokes flow of fluids within porous media at pore scales using sparse point observations and a novel class of physics-informed neural networks, called "physics-informed PointNet" (PIPN). Taking the advantages of PIPN into acco...