SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES FOR COMPUTATIONAL DETECTION METHODS
Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.
1 - 20 . (canceled)
21 . A computer-implemented method for processing electronic slide images, the method comprising:
receiving one or more electronic slide images associated with a tissue specimen;
generating a machine learning prediction model by:
partitioning one of a plurality of training images into a plurality of training tiles for the plurality of training images;
removing at least one of the plurality of training tiles detected to be non-tissue; and
training the machine learning prediction model to infer at least one tile-level prediction using at least one label of a plurality of annotations of the plurality of training images; and
outputting a prediction generated by the machine learning prediction model of at least one label of at least one electronic slide image of the one or more electronic slide images.
22 . The computer-implemented method of claim 21 , wherein the plurality of training tiles that are determined to be non-tissue are further determined to be a background of the tissue specimen.
23 . The computer-implemented method of claim 21 , further comprising:
creating a training tissue mask by detecting at least one tissue region from a background of the one or more electronic slide images; and
detecting a plurality of tissue regions of the one or more electronic slide images and/or plurality of tiles by segmenting the at least one tissue region from the background.
24 . The computer-implemented method of claim 23 , wherein the segmenting comprises using thresholding based on color, color intensity, and/or texture features.
25 . The computer-implemented method of claim 21 , wherein the plurality of training images comprise a plurality of electronic slide images and a plurality of target labels.
26 . The computer-implemented method of claim 21 , wherein using the machine learning prediction model under weak supervision comprises using at least one of multiple-instance learning (MIL), Multiple Instance Multiple Label Learning (MIMLL), self-supervised learning, and unsupervised clustering.
27 . The computer-implemented method of claim 21 , wherein using the machine learning prediction model under weak supervision comprises using at least one of Multiple Instance Multiple Label Learning (MIMLL), self-supervised learning, and unsupervised clustering.
28 . The computer-implemented method of claim 21 , further comprising:
receiving a plurality of predictions of at least one feature from a weakly-supervised tile-level learning module for the plurality of training tiles;
applying the machine learning prediction model to take, as an input, the plurality of predictions of the at least one feature from the weakly-supervised tile-level learning module for the plurality of training tiles; and
predicting a plurality of labels for a slide or a patient specimen, using the plurality of training tiles.
29 . The computer-implemented method of claim 28 , wherein at least one of the plurality of labels is binary, categorical, ordinal or real-valued.
30 . The computer-implemented method of claim 28 , wherein applying the machine learning prediction model to take, as the input, the plurality of predictions of the at least one feature from the weakly-supervised tile-level learning module for the plurality of training tiles comprises a plurality of image features.
31 . The computer-implemented method of claim 21 , wherein the machine learning prediction model predicts at least one label using at least one unseen slide.
32 . A system for processing electronic slide images corresponding to a tissue specimen, the system comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
receiving one or more electronic slide images associated with the tissue specimen;
generating a machine learning prediction model by:
partitioning one of a plurality of training images into a plurality of training tiles for the plurality of training images;
removing at least one of the plurality of training tiles detected to be non-tissue; and
training the machine learning prediction model infer at least one tile-level prediction using at least one label of a plurality of annotations of the plurality of training images; and
outputting a prediction generated by the machine learning prediction model of at least one label of at least one electronic slide image of the one or more electronic slide images.
33 . The system of claim 32 , wherein the plurality of training tiles that are determined to be non-tissue are further determined to be a background of the tissue specimen.
34 . The system of claim 32 , further comprising:
creating a training tissue mask by detecting at least one tissue region from a background of the one or more electronic slide images; and
detecting a plurality of tissue regions of the one or more electronic slide images and/or plurality of tiles by segmenting the at least one tissue region from the background.
35 . The system of claim 34 , wherein the segmenting comprises using thresholding based on color, color intensity, and/or texture features.
36 . The system of claim 32 , wherein the plurality of training electronic slide images comprise a plurality of electronic slide images and a plurality of target labels.
37 . The system of claim 32 , wherein using the machine learning prediction model under weak supervision comprises using at least one of multiple-instance learning (MIL), Multiple Instance Multiple Label Learning (MIMLL), self-supervised learning, and unsupervised clustering.
38 . The system of claim 32 , further comprising:
receiving a plurality of predictions of at least one feature from a weakly-supervised tile-level learning module for the plurality of training tiles;
applying the machine learning prediction model to take, as an input, the plurality of predictions of the at least one feature from the weakly-supervised tile-level learning module for the plurality of training tiles; and
predicting a plurality of labels for a slide or a patient specimen, using the plurality of training tiles.
39 . The system of claim 38 , wherein at least one of the plurality of labels is binary, categorical, ordinal or real-valued.
40 . A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for processing electronic slide images corresponding to a tissue specimen, the method comprising:
receiving one or more electronic slide images associated with a tissue specimen;
generating a machine learning prediction model by:
partitioning one of a plurality of training images into a plurality of training tiles for the plurality of training images;
removing at least one of the plurality of tiles detected to be non-tissue; and
training the machine learning prediction model to infer at least one tile-level prediction using at least one label of a plurality of annotations of the plurality of training images; and
outputting a prediction generated by the machine learning prediction model of at least one label of at least one electronic slide image of the one or more electronic slide images.