DEEP LEARNING AUTOMATED DERMATOPATHOLOGY
First Claim
1. An at least partially computer implemented method of classifying a human cutaneous tissue specimen, the method comprising:
- obtaining a computer readable image of the human tissue sample;
preprocessing the image;
applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained;
applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, wherein the specimen level diagnosis comprises at least one of;
basal cell carcinoma, dermal nevus, or seborrheic keratosis; and
outputting the specimen level diagnosis.
1 Assignment
0 Petitions
Accused Products
Abstract
Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.
-
Citations
20 Claims
-
1. An at least partially computer implemented method of classifying a human cutaneous tissue specimen, the method comprising:
-
obtaining a computer readable image of the human tissue sample; preprocessing the image; applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained; applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, wherein the specimen level diagnosis comprises at least one of;
basal cell carcinoma, dermal nevus, or seborrheic keratosis; andoutputting the specimen level diagnosis. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
-
11. A computer system for classifying a human cutaneous tissue specimen, the computer system comprising at least one electronic processor that executes instructions to perform operations comprising:
-
obtaining a computer readable image of the human tissue sample; preprocessing the image; applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained; applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, wherein the specimen level diagnosis comprises at least one of;
basal cell carcinoma, dermal nevus, or seborrheic keratosis; andoutputting the specimen level diagnosis. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
-
Specification