公开(公告)号：US20210240853A1

公开(公告)日：2021-08-05

申请号：US17267523

申请日：2019-08-23

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Eric Thomas Carlson ,  Mohammad Shahed Sorower ,  Sreramkumar Sitaraman Viswanathan ,  Sreekanth Manakkaparambil Sivanandan ,  Anshul Jain ,  Sunil Ranjan Khuntia ,  Ze He

IPC: G06F21/62 ,  G16H10/60 ,  G16H10/20 ,  G06N20/00

Abstract: The present disclosure is directed to methods and apparatus for centralized de-identification of protected data associated with subjects. In various embodiments, de-identified data may be received (1102) that includes de-identified data set(s) associated with subject(s) that is generated from raw data set(s) associated with the subjects. Each of the raw data set(s) may include identifying feature(s) that are usable to identify the respective subject. At least some of the identifying feature(s) may be absent from or obfuscated in the de-identified data. Labels associated with each of the de-identified data sets may be determined (1104). At least some of the de-identified data sets may be applied (1108) as input across a trained machine learning model to generate respective outputs, which may be compared (1110) to the labels to determine a measure of vulnerability of the de-identified data to re-identification.

公开(公告)号：US12062429B2

公开(公告)日：2024-08-13

申请号：US17270150

申请日：2019-08-21

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Ze He ,  Binyam Gebrekidan Gebre ,  Christine Menking Swisher

IPC: G16H30/40 ,  G06N3/10 ,  G06T7/00 ,  G06V10/22 ,  G06V10/46

CPC classification number: G16H30/40 ,  G06N3/105 ,  G06T7/0012 ,  G06V10/235 ,  G06V10/462 ,  G06T2200/24 ,  G06T2207/20081 ,  G06V2201/031

Abstract: Various embodiments of the present disclosure are directed to a salient medical imaging controller (80) employing an artificial intelligence engine (40) and a graphical user interface (70). In operation, the artificial intelligence engine (40) includes one or more machine learning models (42) trained to render a feature assessment of a medical image. The graphical user interface (70) provides a user interaction with the artificial intelligence engine (40) to manipulate a salient visualization of the feature assessment of the medical image by the machine learning model(s) (42).

公开(公告)号：US20210327563A1

公开(公告)日：2021-10-21

申请号：US17270150

申请日：2019-08-21

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Ze He ,  Binyam Gebrekidan Gebre ,  Christine Menking Swisher

IPC: G16H30/40 ,  G06T7/00 ,  G06N3/08

Abstract: Various embodiments of the present disclosure are directed to a salient medical imaging controller (80) employing an artificial intelligence engine (40) and a graphical user interface (70). In operation, the artificial intelligence engine (40) includes one or more machine learning models (42) trained to render a feature assessment of a medical image. The graphical user interface (70) provides a user interaction with the artificial intelligence engine (40) to manipulate a salient visualization of the feature assessment of the medical image by the machine learning model(s) (42).

公开(公告)号：US20200074101A1

公开(公告)日：2020-03-05

申请号：US16549712

申请日：2019-08-23

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Eric Thomas Carlson ,  Mohammad Shahed Sorower ,  Sreramkumar Sitaraman Viswanathan ,  Manakkaparambil Sivanandan Sreekanth ,  Anshul Jain ,  Sunil Ranjan Khuntia ,  Ze He

IPC: G06F21/62

Abstract: The present disclosure is directed to centralized de-identification of protected data associated with subjects in multiple modalities based on a hierarchal taxonomy of policies and handlers. In various embodiments, data set(s) associated with subject(s) may be received. Each of the data set(s) may contain data points associated with a respective subject. The data points associated with the respective subject may include multiple data types, at least some of which are usable to identify the respective subject. For each respective subject: a classification of each of the data points may be determined in accordance with a hierarchal taxonomy; based on the classifications, respective handlers for the data points may be identified; and each data point of the plurality of data points may be processed using a respective identified handler, thereby de-identifying the plurality of data points associated with the respective subject.