US 12,112,844 B2
Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from medical images
Eli Gibson, Plainsboro, NJ (US); Bogdan Georgescu, Princeton, NJ (US); Pascal Ceccaldi, New York, NY (US); Youngjin Yoo, Princeton, NJ (US); Jyotipriya Das, Plainsboro, NJ (US); Thomas Re, New York, NY (US); Eva Eibenberger, Nuremberg (DE); Andrei Chekkoury, Erlangen (DE); Barbara Brehm, Forchheim (DE); Thomas Flohr, Uehlfeld (DE); Dorin Comaniciu, Princeton, NJ (US); and Pierre-Hugo Trigan, Saint Martin du Manoir (FR)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by SIEMENS HEALTHINEERS AG, Forchheim (DE)
Filed on Mar. 12, 2021, as Appl. No. 17/249,783.
Prior Publication US 2022/0293247 A1, Sep. 15, 2022
Int. Cl. G16H 30/40 (2018.01); G06N 20/00 (2019.01); G16H 50/20 (2018.01)
CPC G16H 30/40 (2018.01) [G06N 20/00 (2019.01); G16H 50/20 (2018.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving one or more input medical images of a patient;
performing a medical imaging analysis task from the one or more input medical images using a machine learning based network, the machine learning based network generating a probability score associated with the medical imaging analysis task;
determining an uncertainty measure representing an error associated with the probability score by:
selecting a calibration function comprising a fixed point defined according to a user selected threshold at which probability scores are totally uncertain;
applying the calibration function to the probability score, and
calculating an entropy of the probability score as the uncertainty measure based on results of the applied calibration function; and
making a clinical decision based on the probability score and the uncertainty measure.