AUTOMATIC ESTIMATION OF TUMOR CELLULARITY USING A DPI AI PLATFORM

A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having...

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Bibliographic Details
Main Authors HUANG, Ko-Kai Albert, SONG, Bi, LIU, Ming-chang
Format Patent
LanguageEnglish
French
German
Published 13.03.2024
Subjects
CALCULATING
COMPUTING
COUNTING
PHYSICS
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Summary:A method for automatically estimating cellularity in a digital pathology slide image includes: extracting patches of interest from the digital pathology slide image; operating on each patch using a trained first deep convolutional neural network (DCNN) to classify that patch as either normal, having an estimated cellularity of 0%, or suspect, having a cellularity roughly estimated to be greater than 0%; operating on each suspect patch using a second DCNN, trained using a deep ordinal regression model, to determine an estimated cellularity score for that suspect patch; and combining the estimated cellularity scores of the patches of interest to provide an estimated cellularity for the digital pathology slide image at a patch-by-patch level.
Bibliography:Application Number: EP20220735981