Eric C Poblenz

US Patent:

20220215915, Jul 7, 2022

Filed:

Jan 11, 2022

Appl. No.:

17/573184

Inventors:

- San Francisco CA, US
Li Yao - San Francisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Jordan Prosky - San Francisco CA, US
Ben Covington - Berkeley CA, US
Anthony Upton - Malvern, AU
Lionel Lints - Oakland CA, US

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 10/60
H04L 67/01
G16H 30/40
G16H 15/00
G06K 9/62
G06T 5/00
G06T 5/50
G06T 7/00
G06T 11/00
G06N 5/04
G16H 30/20
G06N 20/00
G06F 9/54
G06T 7/187
G06T 7/11
G06F 3/0482
G06T 3/40
A61B 5/00
G16H 50/20
G06F 21/62
G06Q 20/14
G16H 40/20
G06F 3/0484
G06Q 10/06
G16H 10/20
G06T 7/10
G06T 11/20
G06F 16/245
G06T 7/44
G06N 20/20
H04L 67/12
G06V 10/22

Abstract:

A model-assisted annotating system is operable to receive a first set of annotation data, corresponding to a broad type of annotation data output. A first training step is performed to train a computer vision model using the first set of annotation data. A second set of annotation data corresponding to the broad type of annotation data output is generated performing an inference function utilizing the computer vision model on medical scans. Additional annotation data further specifies the broad type of annotation data output is received. A second training step is performed to generate an updated computer vision model using set of additional annotation data. A third set of annotation data corresponding to the specified type of annotation data output is generated by performing an updated inference function utilizing the updated computer vision model on medical scans.

US Patent:

20220215918, Jul 7, 2022

Filed:

Mar 25, 2022

Appl. No.:

17/656526

Inventors:

- San Francisco CA, US
Jordan Prosky - San Francisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Kevin Lyman - Fords NJ, US
Ben Covington - Berkeley CA, US
Anthony Upton - Malvern, AU

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 10/60
H04L 67/01
G16H 30/40
G16H 15/00
G06K 9/62
G06T 5/00
G06T 5/50
G06T 7/00
G06T 11/00
G06N 5/04
G16H 30/20
G06N 20/00
G06F 9/54
G06T 7/187
G06T 7/11
G06F 3/0482
G06T 3/40
A61B 5/00
G16H 50/20
G06F 21/62
G06Q 20/14
G16H 40/20
G06F 3/0484
G06Q 10/06
G16H 10/20
G06T 7/10
G06T 11/20
G06F 16/245
G06T 7/44
G06N 20/20
H04L 67/12
G06V 10/22

Abstract:

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

US Patent:

20220180986, Jun 9, 2022

Filed:

Feb 25, 2022

Appl. No.:

17/680493

Inventors:

- San Francisco CA, US
Anthony Upton - Malvern, AU
Li Yao - San Francisco CA, US
Jordan Prosky - San Francisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Chris Croswhite - San Jose CA, US
Ben Covington - Berkeley CA, US

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 10/60
H04L 67/01
G16H 30/40
G16H 15/00
G06K 9/62
G06T 5/00
G06T 5/50
G06T 7/00
G06T 11/00
G06N 5/04
G16H 30/20
G06N 20/00
G06F 9/54
G06T 7/187
G06T 7/11
G06F 3/0482
G06T 3/40
A61B 5/00
G16H 50/20
G06F 21/62
G06Q 20/14
G16H 40/20
G06F 3/0484
G06Q 10/06
G16H 10/20
G06T 7/10
G06T 11/20
G06F 16/245
G06T 7/44
G06N 20/20
H04L 67/12
G06V 10/22

Abstract:

A medical scan header standardization system is operable to determine a plurality of counts for a plurality of entries of at least one of a standard set of fields for headers of a plurality of medical images. A standard set of header entries is determined for at least one of the standard set of fields based on including ones of the entries for the each of the standard set of fields with counts of the plurality of counts that compare favorably to a threshold. One of the standard set of header entries is selected to replace an entry of a field of a header of a medical image. A computer vision model is trained utilizing a training set of images that includes the medical image and the selected one of the standard set of header entries. Inference data for at least one new medical scan is generated based on utilizing the computer vision model.

US Patent:

20210398283, Dec 23, 2021

Filed:

Sep 3, 2021

Appl. No.:

17/446863

Inventors:

- San Francisco CA, US
Kevin Lyman - Fords NJ, US
Ashwin Jadhav - San Francisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Anthony Upton - Malvern, AU

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G06T 7/00
G06K 9/32
G06N 7/00
G06K 9/62
G06T 7/187

Abstract:

A label generating system operates to generate an artificial intelligence model by: training on a training data set that includes the plurality of medical scans with the corresponding global labels; generating testing global probability data by performing an inference function that utilizes the artificial intelligence model on the plurality of medical scans with the corresponding global labels, wherein the testing global probability data indicates a testing set of global probability values corresponding to the set of abnormality classes, and wherein each of the testing set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in each of the plurality of medical scans with the corresponding global labels; comparing the testing set of global probability values to a corresponding confidence threshold for each of the plurality of medical scans selected based on the corresponding one of the global labels; generating an updated training data set by correcting ones of the plurality of medical scans having a corresponding one of the testing set of global probability values that compares unfavorably to the corresponding confidence threshold; and retraining the artificial intelligence model based on the updated training set.

US Patent:

20210366106, Nov 25, 2021

Filed:

Aug 31, 2020

Appl. No.:

17/007200

Inventors:

- San Fransisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Kevin Lyman - Fords NJ, US
Tobi Olatunji - Grand Blanc MI, US
Anthony Upton - Malvern, AU

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G06T 7/00
G16H 30/20
G16H 30/40
G16H 70/20
G16H 70/60
G16H 50/70
G16H 50/20
G16H 15/00
G16H 50/50
G06F 40/279

Abstract:

A system operates by receiving a plurality of medical scans, a plurality of medical labels corresponding to the plurality of medical scans and a plurality of confidence scores corresponding to the plurality of medical labels, wherein each of the plurality of medical labels correspond to one of a set of abnormality classes and wherein the plurality of confidence scores indicate a quantified representation of uncertainty generated via natural language processing of a plurality of medical reports corresponding to the plurality of medical labels; generating a computer vision model by training on the plurality of medical scans and the plurality of medical labels, wherein a model confidence of the computer vision model is calibrated based on the plurality of confidence scores; receiving a new medical scan; generating inference data corresponding to the new medical scan utilizing the computer vision model, wherein the inference data indicates an inferred abnormality in the new medical scan and the model confidence corresponding to the inferred abnormality; and facilitating display of the inference data via an interactive interface.

US Patent:

20210295966, Sep 23, 2021

Filed:

Jun 2, 2021

Appl. No.:

17/336648

Inventors:

- San Francisco CA, US
Li Yao - San Francisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Jordan Prosky - San Francisco CA, US
Ben Covington - Berkeley CA, US
Anthony Upton - Malvern, AU

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 10/60
H04L 29/06
G16H 30/40
G16H 15/00
G06K 9/62
G06T 5/00
G06T 5/50
G06T 7/00
G06T 11/00
G06N 5/04
G16H 30/20
G06N 20/00
G06F 9/54
G06T 7/187
G06T 7/11
G06F 3/0482
G06T 3/40
A61B 5/00
G16H 50/20
G06F 21/62
G06Q 20/14
G16H 40/20
G06F 3/0484
G06Q 10/06
G16H 10/20
G06T 7/10
G06T 11/20
G06F 16/245
G06T 7/44
G06N 20/20
G06K 9/20
H04L 29/08

Abstract:

An intensity transform augmentation system is operable to generate a plurality of sets of augmented images by performing a set of intensity transformation functions on each of a training set of medical scans. Each of the set of intensity transformation functions are based on density properties of corresponding anatomy feature present in the training set of medical scans. A computer vision model is generated by performing a training step on the plurality of sets of augmented images, where each augmented image of a set of augmented images is assigned same output label data based on a corresponding one of the training set of medical scans. Inference data is generated by performing an inference function on a new medical scan by utilizing the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.

US Patent:

20210082547, Mar 18, 2021

Filed:

Sep 16, 2020

Appl. No.:

17/022324

Inventors:

- San Francisco CA, US
Jordan Prosky - San Francisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Kevin Lyman - Fords NJ, US
Lionel Lints - Oakland CA, US
Ben Covington - Berkeley CA, US
Anthony Upton - Malvern, AU

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 10/60
H04L 29/06
G16H 30/40
G16H 15/00
G06K 9/62
G06T 5/00
G06T 5/50
G06T 7/00
G06T 11/00
G06N 5/04
G16H 30/20
G06N 20/00
G06F 9/54
G06T 7/187
G06T 7/11
G06F 3/0482
G06T 3/40
A61B 5/00
G16H 50/20
G06F 21/62
G06Q 20/14
G16H 40/20
G06F 3/0484
G06Q 10/06
G16H 10/20
G06T 7/10
G06T 11/20
G06F 16/245
G06T 7/44
G06N 20/20
G06K 9/20
H04L 29/08

Abstract:

A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.

US Patent:

20210074394, Mar 11, 2021

Filed:

Nov 20, 2020

Appl. No.:

17/100059

Inventors:

- San Francisco CA, US
Li Yao - San Francisco CA, US
Eric C. Poblenz - Palo Alto CA, US
Kevin Lyman - Fords NJ, US
Ben Covington - Berkeley CA, US
Anthony Upton - Malvern, AU

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 10/60
H04L 29/06
G16H 30/40
G16H 15/00
G06K 9/62
G06T 5/00
G06T 5/50
G06T 7/00
G06T 11/00
G06N 5/04
G16H 30/20
G06N 20/00
G06F 9/54
G06T 7/187
G06T 7/11
G06F 3/0482
G06T 3/40
A61B 5/00
G16H 50/20
G06F 21/62
G06Q 20/14
G16H 40/20
G06F 3/0484
G06Q 10/06
G16H 10/20
G06T 7/10
G06T 11/20
G06F 16/245
G06T 7/44
G06N 20/20
G06K 9/20
H04L 29/08

Abstract:

An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.