Healthcare
❯
Image analysis
❯Medical Assistants
❯Computer-aided diagnosis in medical imaging based on machine learning
Generation of computer tomography scans from magnetic resonance images
For:
Oncology hospitals, oncologists
Scope:
Train a model that generates CT images from MRI scans. Synthetic CT images may be used for radiation dose calculation in radiation therapy.
Goal:
Improved Employee Efficiency
Accelerated acquisition of magnetic resonance images
For:
Radiology departments, MRI vendors
Scope:
Innovations in MRI image formation
Goal:
Improved Employee Efficiency
AI platform for chest CT-scan analysis (early stage lung cancer detection)
For:
Healthcare authorities
Scope:
Detecting malignant neoplasms (lungs) on chest CT-scans
Goal:
Anticipate Risks, Improved Employee Efficiency
AI solution for end-to-end processing of cell microscopy images
For:
Biochemical, metabolomics and imaging branches of biomedicine
Scope:
Restoration of naturally distorted microscopy images for following visualization and analysis of meaningful patterns of protein formation inside living cells.
Goal:
Improved Product Development / R&D
Computer-aided diagnosis in medical imaging based on machine learning
Scope:
Detecting image anomalies.
Goal:
Improve Operation Efficiency
Computer-aided diagnosis in medical imaging based on machine learning
Goal:
Improve Operation EfficiencyProblem addressed
Provide an AI method to alleviate the growing burden of histopathological diagnosis by humans.
Scope of use case
Detecting image anomalies.
Description
In histopathological diagnosis, a clinical pathologist
discriminates between normal tissues and cancerous tissues.
However, recently, the shortage of clinical pathologists is
posing increasing burdens on meeting the demands for such
diagnoses, and this is becoming a serious social problem.
Currently, it is necessary to develop new medical
technologies to help reduce these burdens. Therefore, as a
diagnostic support technology, this project proposes an
extended method of higher-order local autocorrelation
(HLAC) feature extraction for classification of
histopathological images into normal and anomaly. The
proposed method can automatically classify cancerous
images as anomaly by using extended geometric invariant
HLAC features with rotation- and reflection-invariant
properties from three-level histopathological images, which
are segmented into nucleus, cytoplasm and background. In
conducted experiments, we demonstrate a reduction in the
rate of not only false-negative errors but also of false-
positive errors, where a normal image is falsely classified as
an image with an anomaly that is suspected as being
cancerous.
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Computer Vision
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