AIMC Topic: Neural Networks, Computer

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Construction of VGG16 Convolution Neural Network (VGG16_CNN) Classifier with NestNet-Based Segmentation Paradigm for Brain Metastasis Classification.

Sensors (Basel, Switzerland)
Brain metastases (BMs) happen often in patients with metastatic cancer (MC), requiring initial and precise diagnosis of BMs, which remains important for medical care preparation and radiotherapy prognostication. Nevertheless, the susceptibility of au...

Matched Filter Interpretation of CNN Classifiers with Application to HAR.

Sensors (Basel, Switzerland)
Time series classification is an active research topic due to its wide range of applications and the proliferation of sensory data. Convolutional neural networks (CNNs) are ubiquitous in modern machine learning (ML) models. In this work, we present a...

Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method.

Scientific reports
We evaluate the diagnostic performance of deep learning artificial intelligence (AI) for bladder cancer, which used white-light images (WLIs) and narrow-band images, and tumor grade prediction of AI based on tumor color using the red/green/blue (RGB)...

Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search.

Nature communications
Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-...

SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation.

Journal of healthcare engineering
A U-Net-based network has achieved competitive performance in retinal vessel segmentation. Previous work has focused on using multilevel high-level features to improve segmentation accuracy but has ignored the importance of shallow-level features. In...

Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a sever...

Learning Transferable Parameters for Unsupervised Domain Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a labeled source domain to an unlabeled target domain under the distribution shift. Thanks to the strong representation ability of deep neural networks, recent remarkable a...

Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models.

Environmental science and pollution research international
Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex an...

Advancing molecular graphs with descriptors for the prediction of chemical reaction yields.

Journal of computational chemistry
Chemical yield is the percentage of the reactants converted to the desired products. Chemists use predictive algorithms to select high-yielding reactions and score synthesis routes, saving time and reagents. This study suggests a novel graph neural n...

Inferring the location of neurons within an artificial network from their activity.

Neural networks : the official journal of the International Neural Network Society
Inferring the connectivity of biological neural networks from neural activation data is an open problem. We propose that the analogous problem in artificial neural networks is more amenable to study and may illuminate the biological case. Here, we st...