David W Knowles

US Patent:

20220189016, Jun 16, 2022

Filed:

Mar 2, 2022

Appl. No.:

17/685115

Inventors:

- Tucson AZ, US
- Palo Alto CA, US
David Knowles - Menlo Park CA, US

International Classification:

G06T 7/00
C12Q 1/6886
G06K 9/62
G06V 20/69

Abstract:

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

US Patent:

20200126222, Apr 23, 2020

Filed:

Oct 21, 2019

Appl. No.:

16/659545

Inventors:

- Tucson AZ, US
- Palo Alto CA, US
David Knowles - Menlo Park CA, US

International Classification:

G06T 7/00
G06K 9/00
G06K 9/62
C12Q 1/6886

Abstract:

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

US Patent:

20170091937, Mar 30, 2017

Filed:

Dec 9, 2016

Appl. No.:

15/374998

Inventors:

- Tucson AZ, US
- Palo Alto CA, US
David Knowles - Menlo Park CA, US

International Classification:

G06T 7/00
G06K 9/00

Abstract:

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.