Convolutional neural networks in medical image understanding: a survey
PulseAugur coverage of Convolutional neural networks in medical image understanding: a survey — every cluster mentioning Convolutional neural networks in medical image understanding: a survey across labs, papers, and developer communities, ranked by signal.
No coverage in the last 90 days.
4 day(s) with sentiment data
-
CNN framework tests General Relativity using gravitational wave data
Researchers have developed a convolutional neural network (CNN) framework to test General Relativity using gravitational wave data. By training the CNN on simulated beyond-GR waveforms, they found that using a response …
-
New OUIDecay method adapts CNN regularization layer-by-layer
Researchers have introduced OUIDecay, a novel adaptive weight decay method for convolutional neural networks. This technique dynamically adjusts regularization strength for each layer based on online activation patterns…
-
New vehicle classifier combines spatial awareness with explainability
Researchers have developed an enhanced vehicle classification system that incorporates spatial awareness of vehicle parts. This new method builds upon a previous approach by constructing spatial probability maps for eac…
-
2D convolutions speed up EEG signal classification
Researchers have explored using 2D spatiotemporal convolutions for classifying EEG signals, an alternative to the common practice of concatenating 1D spatial and temporal convolutions. Their findings indicate that 2D co…
-
Researchers introduce PREMAP2 for efficient neural network certification
Researchers have developed PREMAP2, an enhanced algorithm for approximating neural network preimages, significantly improving scalability and efficiency. This new method extends the capabilities of its predecessor, PREM…
-
GourNet CNN model achieves 97% accuracy in mango leaf disease detection
Researchers have developed GourNet, a Convolutional Neural Network model designed to detect diseases in mango leaves. Trained on the MangoLeafBD dataset, which includes eight classes (seven diseases and one healthy), Go…
-
New MANN method enhances gradient boosting with neural networks for diverse data
Researchers have introduced Multiple Additive Neural Networks (MANN), a novel methodology that replaces decision trees with shallow neural networks in the Gradient Boosting framework. This approach integrates Convolutio…
-
AI study finds lung segmentation vital for COVID-19 X-ray diagnosis
A new study published on arXiv investigates the necessity of data augmentation and lung segmentation for AI-driven COVID-19 detection using chest X-rays. The research, which proposes a methodology called SDL-COVID, foun…
-
Towards interpretable AI with quantum annealing feature selection
Researchers have developed a novel method for interpreting Convolutional Neural Networks (CNNs) in image classification tasks by leveraging quantum annealing for feature selection. This approach identifies the most infl…
-
New framework optimizes deep learning training by separating layers
Researchers have introduced a novel framework called Layer Separation Optimization to address challenges in training deep learning models with cross-entropy loss. This method aims to mitigate the strong nonconvexity iss…
-
Machine learning models reveal geographic data improves insurance claim predictions
Researchers have developed a method to incorporate geographic information into motor insurance claim prediction models, even with limited location data. By utilizing environmental data from OpenStreetMap and CORINE Land…