AIMC Topic: Neural Networks, Computer

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P-ResUnet: Segmentation of brain tissue with Purified Residual Unet.

Computers in biology and medicine
Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medica...

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing.

Scientific reports
In this study, the capabilities of classical and novel integrated machine learning models were investigated to predict sediment discharge (Q) in free-flow flushing. Developed models include Multivariate Linear Regression (MLR), Artificial Neural Netw...

Gene-gene interaction detection with deep learning.

Communications biology
The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasin...

Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor.

Nature communications
The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models ...

Multimodal multi-task deep neural network framework for kinase-target prediction.

Molecular diversity
Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods f...

Comparison of convolutional neural networks for classification of vocal fold nodules from high-speed video images.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVES: Deep learning is in this study used through convolutional neural networks (CNN) to the determination of vocal fold nodules. Through high-speed video (HSV) images and computer-assisted tools, a comparison of convolutional neural network mo...

Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals.

Magnetic resonance in medicine
PURPOSE: A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasib...

Continual learning with attentive recurrent neural networks for temporal data classification.

Neural networks : the official journal of the International Neural Network Society
Continual learning is an emerging research branch of deep learning, which aims to learn a model for a series of tasks continually without forgetting knowledge obtained from previous tasks. Despite receiving a lot of attention in the research communit...

Achieving small-batch accuracy with large-batch scalability via Hessian-aware learning rate adjustment.

Neural networks : the official journal of the International Neural Network Society
We consider synchronous data-parallel neural network training with a fixed large batch size. While the large batch size provides a high degree of parallelism, it degrades the generalization performance due to the low gradient noise scale. We propose ...

Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey.

Sensors (Basel, Switzerland)
With the increasing demand for human-computer interaction and health monitoring, human behavior recognition with device-free patterns has attracted extensive attention. The fluctuations of the Wi-Fi signal caused by human actions in a Wi-Fi coverage ...