SELF-SUPERVISED TRAINING AT SCALE WITH WEAKLY-SUPERVISED LATENT SPACE STRUCTURE

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United States of America

APP PUB NO 20250111656A1
SERIAL NO

18476355

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Abstract

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Systems and methods for performing a medical analysis task using a trained machine learning based task network are provided. Input medical data is received. A medical analysis task is performed using a trained machine learning based task network based on the input medical data. Results of the medical analysis task are output. The trained machine learning based task network is trained by: receiving unannotated training medical data; generating weakly-supervised labels for the unannotated training medical data using one or more trained machine learning based supervised learning networks; training the machine learning based task network for performing the medical analysis task based on 1) the unannotated training medical data, 2) self-supervised labels for the unannotated training medical data learned via self-supervised learning, and 3) the generated weakly-supervised labels for the unannotated training medical data; and outputting the trained machine learning based task network.

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Patent Owner(s)

Patent OwnerAddress
SIEMENS HEALTHINEERS AG91301 FORCHHEIM

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Comaniciu, Dorin Princeton, US 415 12935
Georgescu, Bogdan Princeton, US 203 7044
Ghesu, Florin-Cristian Baiersdorf, DE 26 31
Gulsun, Mehmet Akif Princeton, US 44 631
Narasimha, Murthy Venkatesh Hillsborough, US 9 182
Neumann, Dominik Erlangen, DE 36 464
Serban, Alexandru Constantin Constanta, RO 3 3

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