AIMC Topic: Software

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Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI.

International journal of computer assisted radiology and surgery
PURPOSE: Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown th...

Exploiting defective RRAM array as synapses of HTM spatial pooler with boost-factor adjustment scheme for defect-tolerant neuromorphic systems.

Scientific reports
A crossbar array architecture employing resistive switching memory (RRAM) as a synaptic element accelerates vector-matrix multiplication in a parallel fashion, enabling energy-efficient pattern recognition. To implement the function of the synapse in...

Structural compliance: A new metric for protein flexibility.

Proteins
Proteins are the active players in performing essential molecular activities throughout biology, and their dynamics has been broadly demonstrated to relate to their mechanisms. The intrinsic fluctuations have often been used to represent their dynami...

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

European journal of nuclear medicine and molecular imaging
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be ac...

Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

Laboratory investigation; a journal of technical methods and pathology
Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, patholo...

Keras R-CNN: library for cell detection in biological images using deep neural networks.

BMC bioinformatics
BACKGROUND: A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identif...

MonkeyKing: Adaptive Parameter Tuning on Big Data Platforms with Deep Reinforcement Learning.

Big data
Choosing the right parameter configurations for recurring jobs running on big data analytics platforms is difficult because there can be hundreds of possible parameter configurations to pick from. Even the selection of parameter configurations is bas...

DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.

Genome biology
Dropouts distort gene expression and misclassify cell types in single-cell transcriptome. Although imputation may improve gene expression and downstream analysis to some degree, it also inevitably introduces false signals. We develop DISC, a novel de...

Interactive machine learning for soybean seed and seedling quality classification.

Scientific reports
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on inte...

UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

PloS one
BACKGROUND: Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomed...