Patents by Inventor Andre Aichert
Andre Aichert has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240046468Abstract: A computer-implemented diagnostic-assistance system for medical applications, comprises: an artificial intelligence neural network configured to classify images of an obtained image dataset according to a set of classes; a confidence module configured to generate a confidence measure associated with each of the classified images; a tagging module configured to generate, for the patient, a diagnostic signal based on the generated confidence measures associated with the classified images, wherein the diagnostic signal for the patient is tagged as conclusive if a processed combination of the confidence measures fulfills a condition and tagged as inconclusive if the processed combination of the confidence measures does not fulfill the condition; and an output interface configured to output the diagnostic signal, wherein if the diagnostic signal is conclusive the classification result is released, and if the diagnostic signal is inconclusive an additional diagnostic analysis is triggered.Type: ApplicationFiled: August 2, 2023Publication date: February 8, 2024Applicant: Siemens Healthcare GmbHInventors: Marvin TEICHMANN, Andre AICHERT, Rico BRENDTKE
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Publication number: 20230342927Abstract: Various examples of the disclosure pertain to using whole-slide images that depict healthy tissue for a training process for at least one machine-learning algorithm for digital pathology. For instance, an autoencoder neural network can be trained based on the healthy tissue.Type: ApplicationFiled: April 18, 2023Publication date: October 26, 2023Applicants: Siemens Healthcare GmbH, Georg-August-Universitaet Goettingen Stiftung oeffentlichen Rechts Universitaetsmadzin GoettingenInventors: Andre AICHERT, Marvin TEICHMANN, Hanibal BOHNENBERGER, Birgi TAMERSOY
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Publication number: 20230306606Abstract: One or more example embodiments are methods and corresponding systems for providing a training data set for training a segmentation algorithm for segmenting whole-slide images in digital pathology as well as the use of the training data and corresponding ML segmentation algorithms. For example, a first segmentation of a whole slide image is refined based on an automatically generated annotation which has a higher level of detail than the first segmentation. A second segmentation results, which may be used as a ground truth for training the ML segmentation algorithm on the basis of the whole slide image.Type: ApplicationFiled: March 20, 2023Publication date: September 28, 2023Applicant: Siemens Healthcare GmbHInventors: Andre AICHERT, Marvin TEICHMANN, Arnaud Arinda ADIYOSO
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Publication number: 20230282011Abstract: A set of pre-annotated medical images is received, and the received set is processed by automatically: training an AI-based uncertainty model using the received set as training data; processing medical images of the received set by determining classified segments and/or uncertainty regions in the medical images using the trained AI-based uncertainty model; selecting at least a part of the processed medical images including classified segments and/or uncertainty regions based on the processing result; and presenting the selected part of processed medical images to a human expert. Furthermore, a modified received set including additional annotations created by the human expert is received.Type: ApplicationFiled: February 27, 2023Publication date: September 7, 2023Applicants: Siemens Healthcare GmbH, Georg-August-Universitaet Goettingen Stiftung oeffentlichen Rechts Universitaetsmedizin GoettingenInventors: Marvin TEICHMANN, Andre AICHERT, Hanibal BOHNENBERGER
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Publication number: 20230274534Abstract: Various disclosed examples pertain to digital pathology, more specifically to training of a segmentation algorithm for segmenting whole-slide images depicting tissue of multiple types. An initial annotation of a whole-slide image is refined to yield a refined annotation based on which parameters of the segmentation algorithm can be set. Techniques of patch-wise weak supervision can be employed for such refinement.Type: ApplicationFiled: February 23, 2023Publication date: August 31, 2023Applicant: Siemens Healthcare GmbHInventors: Andre AICHERT, Marvin TEICHMANN, Birgi TAMERSOY, Martin KRAUS, Arnaud Arindra ADIYOSO
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Publication number: 20230230704Abstract: One or more example embodiments of the present invention is based on a computer-implemented method for providing molecular data. The method comprises receiving a computed tomography image of at least a part of a lung of a patient, wherein the computed tomography image depicts at least one lung nodule. The molecular data is determined by processing first input data with a first trained function, wherein the first input data is based on the computed tomography image, and wherein the molecular data relates to a biomarker within at least one of a genome of the patient, a transcriptome of the patient, a proteome of the patient or a metabolome of the patient. Furthermore, the molecular data is provided. Providing the molecular data can comprise at least one of displaying, transmitting or storing the molecular data.Type: ApplicationFiled: January 17, 2023Publication date: July 20, 2023Applicant: Siemens Healthcare GmbHInventors: Arnaud Arindra ADIYOSO, Andre Aichert, Marvin Teichmann, Tobias Heimann
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Patent number: 11699233Abstract: Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.Type: GrantFiled: March 28, 2022Date of Patent: July 11, 2023Assignee: SIEMENS HEALTHCARE GMBHInventors: Marvin Teichmann, Andre Aichert, Birgi Tamersoy, Martin Kraus, Arnaud Arindra Adiyoso, Tobias Heimann
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Publication number: 20220319000Abstract: Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.Type: ApplicationFiled: March 28, 2022Publication date: October 6, 2022Applicant: Siemens Healthcare GmbHInventors: Marvin TEICHMANN, Andre AICHERT, Birgi TAMERSOY, Martin KRAUS, Arnaud Arindra ADIYOSO, Tobias HEIMANN
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Patent number: 11048796Abstract: A system and a method are provided for a parameter update. In an embodiment, the method includes obtaining, by a first entity, a function and parameter data from a second entity; selecting data samples provided by the first entities; providing a plurality of mutually isolated computing instances; assigning and providing the selected data samples to the computing instances; calculating, within each computing instance, results of the function; calculating averages over the results; determining whether the function fulfils a security criterion, and, if so: providing the calculated average for the gradient of the loss function and/or the calculated average of the output value and/or updated parameter data to the second entity.Type: GrantFiled: July 9, 2020Date of Patent: June 29, 2021Assignee: SIEMENS HEALTHCARE GMBHInventors: Martin Kraus, Andre Aichert
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Patent number: 10977839Abstract: A method for determining assignment data is carried out in a method for determining a geometry calibration. The 3D calibration phantom features a calibration object with a number of calibration elements, which are arranged so that a descriptor based on the spatial arrangement is projectively invariant. Based upon the descriptor the calibration elements mapped in the 2D transmission element can be assigned to the calibration elements of the calibration object, so that the geometry calibration is determined on the basis of this assignment and the arrangement of the calibration elements in the three-dimensional space as well as on the 2D image.Type: GrantFiled: May 9, 2019Date of Patent: April 13, 2021Assignee: SIEMENS HEALTHCARE GMBHInventors: Andre Aichert, Andreas Maier, Tobias Wuerfl
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Publication number: 20210019395Abstract: A system and a method are provided for a parameter update. In an embodiment, the method includes obtaining, by a first entity, a function and parameter data from a second entity; selecting data samples provided by the first entities; providing a plurality of mutually isolated computing instances; assigning and providing the selected data samples to the computing instances; calculating, within each computing instance, results of the function; calculating averages over the results; determining whether the function fulfils a security criterion, and, if so: providing the calculated average for the gradient of the loss function and/or the calculated average of the output value and/or updated parameter data to the second entity.Type: ApplicationFiled: July 9, 2020Publication date: January 21, 2021Applicant: Siemens Healthcare GmbHInventors: Martin KRAUS, Andre AICHERT
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Publication number: 20190355156Abstract: A method for determining assignment data is carried out in a method for determining a geometry calibration. The 3D calibration phantom features a calibration object with a number of calibration elements, which are arranged so that a descriptor based on the spatial arrangement is projectively invariant. Based upon the descriptor the calibration elements mapped in the 2D transmission element can be assigned to the calibration elements of the calibration object, so that the geometry calibration is determined on the basis of this assignment and the arrangement of the calibration elements in the three-dimensional space as well as on the 2D image.Type: ApplicationFiled: May 9, 2019Publication date: November 21, 2019Applicant: Siemens Healthcare GmbHInventors: Andre Aichert, Andreas Maier, Tobias Wuerfl