AIMC Topic: Automation

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Convolutional neural network for cell classification using microscope images of intracellular actin networks.

PloS one
Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using ...

Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description.

Medical physics
PURPOSE: Developing automated methods to identify task-driven quality assurance (QA) procedures is key toward increasing safety, efficacy, and efficiency. We investigate the use of machine learning (ML) methods for possible visualization, automation,...

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.

Scientific reports
Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteri...

Automated detection of koalas using low-level aerial surveillance and machine learning.

Scientific reports
Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduce...

AEB effectiveness evaluation based on car-to-cyclist accident reconstructions using video of drive recorder.

Traffic injury prevention
OBJECTIVE: Though autonomous emergency braking (AEB) systems for car-to-cyclist collisions have been under development, an estimate of the benefit of AEB systems based on an analysis of accident data is needed for further enhancing their development....

Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

PloS one
Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is key to full patient recovery. The Microscopic Observed Drug Susceptibility (MODS) is a test to diagnos...

Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis.

Artificial intelligence in medicine
INTRODUCTION: Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the ...

Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning.

Physics in medicine and biology
This paper presents a new approach to automatic three-dimensional (3D) cephalometric annotation for diagnosis, surgical planning, and treatment evaluation. There has long been considerable demand for automated cephalometric landmarking, since manual ...

Automated Triage Radiation Biodosimetry: Integrating Imaging Flow Cytometry with High-Throughput Robotics to Perform the Cytokinesis-Block Micronucleus Assay.

Radiation research
The cytokinesis-block micronucleus (CBMN) assay has become a fully-validated and standardized method for radiation biodosimetry. The assay is typically performed using microscopy, which is labor intensive, time consuming and impractical after a large...

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.

Medical physics
PURPOSE: Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other ...