HYBRID CLASSIFIER TRAINING FOR FEATURE EXTRACTION

The use of machine learning (ML) can provide good results in annotating features present in images. However training the ML process can require a large amount of training images that have had the individual features correctly annotated. An ML process and training technique is described that can trai...

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Bibliographic Details
Main Authors KATCHINSKIY, NIR, CEROICI, CHRISTOPHER
Format Patent
LanguageEnglish
French
Published 05.05.2023
Subjects
BANDAGES, DRESSINGS OR ABSORBENT PADS
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF,TUBULAR STRUCTURES OF THE BODY, E.G. STENTS
FILTERS IMPLANTABLE INTO BLOOD VESSELS
FIRST-AID KITS
FOMENTATION
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
HUMAN NECESSITIES
HYGIENE
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
MEDICAL OR VETERINARY SCIENCE
ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES
PHYSICS
PROSTHESES
TREATMENT OR PROTECTION OF EYES OR EARS
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Summary:The use of machine learning (ML) can provide good results in annotating features present in images. However training the ML process can require a large amount of training images that have had the individual features correctly annotated. An ML process and training technique is described that can train and use a classifier in order to annotate features in an image. The training uses saliency loss propagation (SLP) to train the classifier on portions of images that include important features.
Bibliography:Application Number: CA20213137612