Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from medical images

Patent metadata

Field	Value
Publication number	US-12112844-B2
Application number	US-202117249783-A
Country	US
Kind code	B2
Filing date	Mar 12, 2021
Priority date	Mar 12, 2021
Publication date	Oct 8, 2024
Grant date	Oct 8, 2024

Abstract

Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

First claim

The invention claimed is:

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.

2. The computer-implemented method of claim 1 , wherein the medical imaging analysis task comprises at least one of detection, subtyping, or segmentation of an intracranial hemorrhage of the patient.

3. The computer-implemented method of claim 1 , wherein making a clinical decision based on the probability score and the uncertainty measure comprises:
stratifying the patient into one of a plurality of patient groups based on the probability score and the uncertainty measure.

4. The computer-implemented method of claim 3 , wherein the medical imaging analysis task comprises detection of an intracranial hemorrhage of the patient, and the plurality of patient groups comprises a high confidence positive detection patient group, a high confidence negative detection patient group, and a low confidence patient group.

5. The computer-implemented method of claim 1 , wherein making a clinical decision based on the probability score and the uncertainty measure comprises:
determining whether to treat the patient based on the probability score and the uncertainty measure.

6. The computer-implemented method of claim 1 , wherein making a clinical decision based on the probability score and the uncertainty measure comprises:
determining whether to perform a clinical test on the patient based on the probability score and the uncertainty measure.

7. The computer-implemented method of claim 1 , wherein making a clinical decision based on the probability score and the uncertainty measure comprises:
prioritizing a worklist of a radiologist based on the probability score and the uncertainty measure.

8. An apparatus comprising:
means for receiving one or more input medical images of a patient;
means for 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;
means for 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

means for making a clinical decision based on the probability score and the uncertainty measure.

9. The apparatus of claim 8 , wherein the means for making a clinical decision based on the probability score and the uncertainty measure comprises:
means for stratifying the patient into one of a plurality of patient groups based on the probability score and the uncertainty measure.

10. The apparatus of claim 9 , wherein the medical imaging analysis task comprises detection of an intracranial hemorrhage of the patient, and the plurality of patient groups comprises a high confidence positive detection patient group, a high confidence negative detection patient group, and a low confidence patient group.

11. A non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations 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.

12. The non-transitory computer readable medium of claim 11 , wherein the medical imaging analysis task comprises at least one of detection, subtyping, or segmentation of an intracranial hemorrhage of the patient.

13. The non-transitory computer readable medium of claim 11 , wherein making a clinical decision based on the probability score and the uncertainty measure comprises:
determining whether to treat the patient based on the probability score and the uncertainty measure.

14. The non-transitory computer readable medium of claim 11 , wherein making a clinical decision based on the probability score and the uncertainty measure comprises:
determining whether to perform a clinical test on the patient based on the probability score and the uncertainty measure.

15. The non-transitory computer readable medium of claim 11 , wherein making a clinical decision based on the probability score and the uncertainty measure comprises:
prioritizing a worklist of a radiologist based on the probability score and the uncertainty measure.

Assignees

• Siemens Healthineers Ag

Inventors

• Gibson Eli
• Georgescu Bogdan
• Ceccaldi Pascal
• Yoo Youngjin
• Das Jyotipriya
• Re Thomas
• Eibenberger Eva
• Chekkoury Andrei
• Brehm Barbara
• Flohr Thomas
• Comaniciu Dorin
• Trigan Pierre-Hugo

Classifications

• G06T12/00 Primary

  Tomographic reconstruction from projections · CPC title

• G06N20/00

  Machine learning · CPC title

• G16H50/20 Primary

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• A61B6/5211

  involving processing of medical diagnostic data · CPC title