AIMC Topic: Neural Networks, Computer

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Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking.

IEEE/ACM transactions on computational biology and bioinformatics
Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases i...

Theory of deep convolutional neural networks III: Approximating radial functions.

Neural networks : the official journal of the International Neural Network Society
We consider a family of deep neural networks consisting of two groups of convolutional layers, a downsampling operator, and a fully connected layer. The network structure depends on two structural parameters which determine the numbers of convolution...

KerNet: A Novel Deep Learning Approach for Keratoconus and Sub-Clinical Keratoconus Detection Based on Raw Data of the Pentacam HR System.

IEEE journal of biomedical and health informatics
Keratoconus is one of the most severe corneal diseases, which is difficult to detect at the early stage (i.e., sub-clinical keratoconus) and possibly results in vision loss. In this paper, we propose a novel end-to-end deep learning approach, called ...

Symbolic Deep Networks: A Psychologically Inspired Lightweight and Efficient Approach to Deep Learning.

Topics in cognitive science
The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept i...

An artificial neural network chip based on two-dimensional semiconductor.

Science bulletin
Recently, research on two-dimensional (2D) semiconductors has begun to translate from the fundamental investigation into rudimentary functional circuits. In this work, we unveil the first functional MoS artificial neural network (ANN) chip, including...

Predicting crop root concentration factors of organic contaminants with machine learning models.

Journal of hazardous materials
Accurate prediction of uptake and accumulation of organic contaminants by crops from soils is essential to assessing human exposure via the food chain. However, traditional empirical or mechanistic models frequently show variable performance due to c...

Recovering SWI-filtered phase data using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep neural network to recover filtered phase from clinical MR phase images to enable the computation of QSMs.

Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) have recently emerged as a new generation of low-power deep neural networks due to sparse, asynchronous, and binary event-driven processing. Most previous deep SNN optimization methods focus on static datasets (e.g., MN...

AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs.

Physical and engineering sciences in medicine
According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical f...

Deep Neural Networks for Detection and Location of Microseismic Events and Velocity Model Inversion from Microseismic Data Acquired by Distributed Acoustic Sensing Array.

Sensors (Basel, Switzerland)
Fiber-optic cables have recently gained popularity for use as Distributed Acoustic Sensing (DAS) arrays for borehole microseismic monitoring due to their physical robustness as well as high spatial and temporal resolutions. As a result, the sensors r...