AIMC Topic: Deep Learning

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Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.

EBioMedicine
BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of b...

Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization.

Computational intelligence and neuroscience
Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhanc...

DeepAdd: Protein function prediction from k-mer embedding and additional features.

Computational biology and chemistry
With the application of new high throughput sequencing technology, a large number of protein sequences is becoming available. Determination of the functional characteristics of these proteins by experiments is an expensive endeavor that requires a lo...

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.

International journal of medical informatics
OBJECTIVE: This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a he...

Effectiveness of groundwater heavy metal pollution indices studies by deep-learning.

Journal of contaminant hydrology
Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking water safety as well as human and animal health. Therefore, evaluation of groundwater HM pollution is essential to prevent accompanying hazardous ecological i...

LncMirNet: Predicting LncRNA-miRNA Interaction Based on Deep Learning of Ribonucleic Acid Sequences.

Molecules (Basel, Switzerland)
Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing that the interaction patterns between lncRNAs and miRNAs are highly related...

Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock.

BMC biology
BACKGROUND: Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative ...

Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis.

BioMed research international
Methylation of the O-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of M...

Biological batch normalisation: How intrinsic plasticity improves learning in deep neural networks.

PloS one
In this work, we present a local intrinsic rule that we developed, dubbed IP, inspired by the Infomax rule. Like Infomax, this rule works by controlling the gain and bias of a neuron to regulate its rate of fire. We discuss the biological plausibilit...