AIMC Topic: Deep Learning

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CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.

BMC bioinformatics
BACKGROUND: The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using ...

Predicting RNA SHAPE scores with deep learning.

RNA biology
Secondary structure prediction approaches rely typically on models of equilibrium free energies that are themselves based on in vitro physical chemistry. Recent transcriptome-wide experiments of in vivo RNA structure based on SHAPE-MaP experiments pr...

A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance.

European radiology
OBJECTIVES: To develop a deep learning algorithm for automated detection and localization of intracranial aneurysms on time-of-flight MR angiography and evaluate its diagnostic performance.

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

Computers in biology and medicine
MOTIVATION: Brain or central nervous system cancer is the tenth leading cause of death in men and women. Even though brain tumour is not considered as the primary cause of mortality worldwide, 40% of other types of cancer (such as lung or breast canc...

Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

Laboratory investigation; a journal of technical methods and pathology
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including ...

CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.

NeuroImage
In this study, we developed a multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox (CAST) for automated segmentation of hippocampal subfields. Although training CAST required approximately three days on a...

Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography.

Occupational and environmental medicine
OBJECTIVES: To investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.

Deciphering image contrast in object classification deep networks.

Vision research
The ultimate goal of neuroscience is to explain how complex behaviour arises from neuronal activity. A comparable level of complexity also emerges in deep neural networks (DNNs) while exhibiting human-level performance in demanding visual tasks. Unli...

Breast ultrasound region of interest detection and lesion localisation.

Artificial intelligence in medicine
In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among human exp...