AIMC Topic: Neural Networks, Computer

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Few-Shot Learning for Low-Data Drug Discovery.

Journal of chemical information and modeling
The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of c...

Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?

Joint bone spine
The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a ...

Deep convolutional feature details for better knee disorder diagnoses in magnetic resonance images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Convolutional neural networks (CNNs) applied to magnetic resonance imaging (MRI) have demonstrated their ability in the automatic diagnosis of knee injuries. Despite the promising results, the currently available solutions do not take into account th...

Vehicular Environment Identification Based on Channel State Information and Deep Learning.

Sensors (Basel, Switzerland)
This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connec...

Enhanced Tooth Region Detection Using Pretrained Deep Learning Models.

International journal of environmental research and public health
The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient's panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant...

DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.

Nature communications
The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a pr...

Multi-modal wound classification using wound image and location by deep neural network.

Scientific reports
Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide on an optimal treatment procedure. This study develo...

SNAL: sensitive non-associative learning network configuration for the automatic driving strategy.

Scientific reports
Nowadays, there is a huge gap between autonomous vehicles and mankind in terms of the decision response against some dangerous scenarios, which would has stressed the potential users out and even made them nervous. To efficiently identify the possibl...

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).

Scientific reports
Treatment of blood smears with Wright's stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leukocyte disorders, a clinical pathologist must perform a tedious, manual process of locating and identi...

Bias reduction in representation of histopathology images using deep feature selection.

Scientific reports
Appearing traces of bias in deep networks is a serious reliability issue which can play a significant role in ethics and generalization related concerns. Recent studies report that the deep features extracted from the histopathology images of The Can...