US 11,669,636 B2
Medical data collection for machine learning
Arne Ewald, Hamburg (DE); Tim Nielsen, Hamburg (DE); Karsten Sommer, Hamburg (DE); Irina Waechter-Stehle, Hamburg (DE); Christophe Michael Jean Schülke, Hamburg (DE); Frank Michael Weber, Hamburg (DE); Rolf Jürgen Weese, Hamburg (DE); and Jochen Peters, Norderstedt (DE)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Filed on Mar. 10, 2020, as Appl. No. 16/814,249.
Claims priority of application No. 19161895 (EP), filed on Mar. 11, 2019.
Prior Publication US 2020/0293690 A1, Sep. 17, 2020
Int. Cl. G06F 21/62 (2013.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G06N 20/00 (2019.01)
CPC G06F 21/6254 (2013.01) [G06N 20/00 (2019.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01)] 9 Claims
OG exemplary drawing
 
1. A system for data collection for machine learning of a machine learnable model, the system comprising:
one or more hardware processors;
a communication interface to another entity associated with training of a machine learnable model; and
a memory storing instructions to:
receive a request for training data from the other entity;
access:
medical image data of at least one patient and label data defining labels associated with the medical image data; and
privacy policy data defining one or more computer-readable criteria for limiting a selection of the medical image data to a subset of the medical image data to obfuscate an identity of the at least one patient;
verify whether the request is in compliance with the privacy policy data;
based on the one or more computer-readable criteria, automatically perform, by the one or more hardware processors, the selection of the medical image data and an associated selection of the label data to obtain privacy policy-compliant training data, wherein the selection is limited to one or more image regions of the medical image data; and
via the communication interface, transmit the privacy policy-compliant training data to the other entity to enable the machine learnable model to be trained on the basis of the privacy policy-compliant training data.