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US 11,893,811 B2Granted
Method for object detection using hierarchical deep learning
AssigneeCarnegie Mellon University
InventorsDaniel Clymer, Jonathan Cagan, Philip LeDuc, Liron Pantanowitz, Janet Catov
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Abstract
A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.
Claims20
A method comprising:
- obtaining a high-resolution whole slide image of a post-birth placenta;
- analyzing the whole slide image, using a trained machine learning model, to identify one or more blood vessels in the placenta, the analysis occurring at a lower resolution than the native resolution of the whole slide image;
- classifying the identified blood vessels at a higher resolution using a trained machine learning classifier that outputs a latent vector for each classified blood vessel:
- aggregating the latent vectors for a predetermined number of classified blood vessels;
- pooling the aggregated latent vectors by calculating a maximum or minimum of the data for each node of a feature map of the machine learning classifier;
- reducing the dimension on the pooled aggregated latent vectors to produce a reduced dimension latent vector;
- and
- performing a binary classification for each whole slide image based on the latent vector.
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A method comprising:
- obtaining one or more whole slide images of a post-birth placenta;
- separating the whole slide image into a plurality of image patches;
- analyzing each image patch at a lower resolution than the native resolution of the whole slide image using a trained machine learning model, to identify one or more blood vessels in the image patch;
- classifying each identified blood vessel in the image patch at a higher resolution, using a trained machine learning classifier;
- aggregating latent vectors produced by the machine learning classifier for a predetermined number of classified blood vessels;
- pooling the aggregated latent vectors by calculating a maximum or minimum for each node of a feature map of the machine learning classifier;
- and
- performing principal component analysis on the pooled aggregated latent vectors to produce a reduced dimension latent vector.
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Dependencies are inferred from the claim text.