AIMC Topic: Deep Learning

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The emerging role of deep learning in cytology.

Cytopathology : official journal of the British Society for Clinical Cytology
Deep learning (DL) is a component or subset of artificial intelligence. DL has contributed significant change in feature extraction and image classification. Various algorithmic models are used in DL such as a convolutional neural network (CNN), recu...

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

Medical image analysis
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed cli...

Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging.

Scientific reports
The classification of Alzheimer's disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various po...

Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells.

eLife
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniq...

Deep-learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria.

The European respiratory journal
RATIONALE: While American Thoracic Society (ATS)/European Respiratory Society (ERS) quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and lea...

A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.

PloS one
BACKGROUND: A powerful risk model allows clinicians, at the bedside, to ensure the early identification of and decision-making for patients showing signs of developing physiological instability during treatment. The aim of this study was to enhance t...

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture.

PloS one
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such a...

Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity.

PloS one
Determination of pile bearing capacity is essential in pile foundation design. This study focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network (DLNN) algorithm to predict the bearing capacity of driven pile. For this...

Reducing scan time of paediatric Tc-DMSA SPECT via deep learning.

Clinical radiology
AIM: To investigate the feasibility of reducing the scan time of paediatric technetium 99m (Tc) dimercaptosuccinic acid (DMSA) single-photon-emission computed tomographic (SPECT) using a deep learning (DL) method.