AIMC Topic: Deep Learning

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Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.

European radiology
OBJECTIVES: To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR).

Identification of antimicrobial peptides from the human gut microbiome using deep learning.

Nature biotechnology
The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LST...

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Computers in biology and medicine
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) ima...

INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network.

ISA transactions
Testing is one of the important methodologies used by various countries in order to fight against COVID-19 infection. The infection is considered as one of the deadliest ones although the mortality rate is not very high. COVID-19 infection is being c...

Compound-protein interaction prediction by deep learning: Databases, descriptors and models.

Drug discovery today
The screening of compound-protein interactions (CPIs) is one of the most crucial steps in finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address intrinsic limitations of traditional HTS and virtual screening with th...

Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies.

International journal of computer assisted radiology and surgery
PURPOSE: The registration of medical images often suffers from missing correspondences due to inter-patient variations, pathologies and their progression leading to implausible deformations that cause misregistrations and might eliminate valuable inf...

Artificial intelligence for histological subtype classification of breast cancer: combining multi-scale feature maps and the recurrent attention model.

Histopathology
AIMS: The aim of this study was to apply a two-stage deep model combining multi-scale feature maps and the recurrent attention model (RAM) to assist with the pathological diagnosis of breast cancer histological subtypes by the use of whole slide imag...

Deep-Learning-Based Parking Area and Collision Risk Area Detection Using AVM in Autonomous Parking Situation.

Sensors (Basel, Switzerland)
In this paper, I propose a bird eye view image detection method for parking areas and collision risk areas at the same time in parking situations. Deep learning algorithms using area detection and semantic segmentation were used. The main architectur...

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach.

Sensors (Basel, Switzerland)
The periodic inspection of railroad tracks is very important to find structural and geometrical problems that lead to railway accidents. Currently, in Pakistan, rail tracks are inspected by an acoustic-based manual system that requires a railway engi...