Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Healthineers Ag

Siemens Healthcare Diagnostics Gmbh

Siemens Corporation

Other

Beth Israel Deaconess Medical Center, Inc.

Yale University

The Cleveland Clinic Foundation

Technische Universität München

Siemens Ag Osterreich

Institut National De Recherche En Informatique Et En Automatique

Inria

Ali Kamen

Dorin Comaniciu

Bogdan Georgescu

Tommaso Mansi

Puneet Sharma

Saikiran Rapaka

Viorel Mihalef

Bin Lou

Noha Youssry El-Zehiry

Vivek Kumar Singh

Tiziano Passerini

Lucian Mihai Itu

Xudong Zheng

Shanhui Sun

Mamadou Diallo

Berthold Kiefer

Rui Liao

David Jean Winkel

Terrence Chen

Yefeng Zheng

Frank Sauer

Sasa Grbic

Hien Nguyen

Hari Sundar

Lance Anthony Ladic

Parmeshwar Khurd

Bibo Shi

Ankur Kapoor

Robert Grimm

Wolfgang Wein

Leo John Grady

Jan Boese

Atilla Peter Kiraly

Thomas Redel

Michael Scheuering

Shun P Miao

John V Frangioni

Gianluca Paladini

Daguang Xu

Thomas Pheiffer

Julian Krebs

Florin Cristian Ghesu

Heinrich Von Busch

Boris Mailhe

David D Liu

Li Zhang

Matthias John

Marie-Pierre Jolly

Oliver Hayden

Fernando Vega-Higuera

Dominik Bernhardt

Dominik Neumann

Martin Requardt

Jin-hyeong Park

Huseyin Tek

Thomas Boettger

Nicholas Ayache

Clifford R Weiss

Alexander Michael Gigler

Lukas Richter

Matthias Ugele

Fernando Vega

Mariappan S Nadar

Andreas Greiser

Oliver Schmidt

Bernhard Geiger

Maximilian Fleischer

Liexiang Fan

Chenyang Xu

Manfred Stanzel

Christophe Chefd'hotel

Ralf Nanke

Estelle Camus

Stefan Kluckner

Gabriel Haras

Kai Ma

Maneesh Kumar Singh

Ingmar Voigt

Zhoubing Xu

Jens Fehre

Razvan Ionasec

Andreas Keller

Jingdan Zhang

Helene C Houle

Anton Schick

Jens Guehring

Jerome Declerck

Peng Wang

Alexander Brost

Sebastian Vogt

Fred S Azar

Bruce S Spottiswoode

Peter Gall

Alois Nöttling

Ahmet Tuysuzoglu

Olivier Ecabert

Andrzej Milkowski

John Benson

Gaby Marquardt

Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Localization and classification of abnormalities in medical images

Systems and methods for automatically registering a first input medical image and a second input medical image are provided. The first input medical image in a first modality and the second input medical image in a second modality are received. One or more objects of interest are segmented from the first input medical image to generate a first segmentation map and one or more objects of interest are segmented from the second input medical image to generate a second segmentation map. A first point cloud is extracted from the first segmentation map and a second point cloud is extracted from the second segmentation map. A transformation for aligning the first point cloud and the second point cloud is determined to register the first input medical image and the second input medical image. The transformation is output.

Automatic MR-US prostate image fusion through semi-supervised constrained learning

For uncertainty estimation for a machine-learned model prediction in medical imaging, a distribution in latent space is sampled to determine uncertainty in machine-learned model prediction. For example, a segmentation is output by a machine-learned model in response to input of multi-parametric data. A latent space generated by the machine-learned model is used to estimate the uncertainty of the segmentation, such as a segmentation of a prostate lesion. The model of Kohl, et al., may be used as the variational auto encoder generates a latent space representing the distribution of the training data, which latent space may be used to determine uncertainty.

Uncertainty estimation in medical imaging

Systems and methods for automatically staging non-small cell lung cancer are provided. Patient data relating to a cancer of a patient is received. The patient data comprises one or more medical images and one or more biopsy reports. A T-stage of the cancer is determined based on a location and a size of one or more tumors of the cancer determined using the patient data. An N-stage of the cancer is determined by combining a metastasis evaluation of the cancer to regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports. An M-stage of the cancer is determined based on a metastasis evaluation of the cancer to anatomical structures based on the patient data. The T-stage, the N-stage, and the M-stage are output.

Automatic staging of non-small cell lung cancer from medical imaging and biopsy reports

For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. Deep learning may result in features more predictive of outcome than handcrafted features. More comprehensive learning may be provided by using multi-task learning where one of the tasks (e.g., segmentation, non-image data, and/or feature extraction) is unsupervised and/or draws on a greater number of training samples than available for outcome prediction alone.

Decision support system for medical therapy planning

Systems and methods for determining whether input medical images are out-of-distribution of training images on which a machine learning based medical imaging analysis network is trained are provided. One or more input medical images of a patient are received. One or more reconstructed images of the one or more input medical images are generated using a machine learning based reconstruction network. It is determined whether the one or more input medical images are out-of-distribution from training images on which a machine learning based medical imaging analysis network is trained based on the one or more input medical images and the one or more reconstructed images. The determination of whether the one or more input medical images are out-of-distribution from the training images is output.

Out-of-distribution detection for artificial intelligence systems for prostate cancer detection

In an method for training artificial intelligence entities (AIE) for abnormality detection, medical imaging data of the human organ is provided as training data having training samples, the medical imaging data including imaging results from different types of imaging techniques for each training sample of the training data, a pre-trained or randomly initialized AIE is provided, and the AIE is trained using the provided training samples. The training may include, for at least one training sample, a first loss function for a sub-structure of the AIE is calculated independently of a first spatial region of the human organ, and, for a training sample, a second loss function for a sub-structure of the AIE is calculated independently of a second spatial region of the human organ. The AIE may be trained using the calculated first loss function and the calculated second loss function.

Methods and systems for improved abnormality detection in medical imaging data

For autonomous MR scanning for a given medical test, a simplified MR scanner may be used without or will little input or control by a technologist (e.g., by a physician, radiologist, or person trained in MR scanner operation). The MR scanner autonomously positions, scans, checks quality, analyzes, and/or outputs an answer to a diagnostic question with or without an MR image. Scan analysis, based on artificial intelligence, allows for on-going or on-the-fly alteration of the scanning configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both position of the patient relative to the MR scanner and localization of the scan by the MR scanner are jointly solved. Sensors may sense a patient in a scan position where the reduced radio frequency requirements allow for a more open bore.

Autonomous magnetic resonance scanning for a given medical test

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Method and system for image registration using an intelligent artificial agent

Similar pre-stored medical datasets are identified by comparison with a current case dataset. A current case dataset is provided and includes radiological data of a patient. A number of pre-stored medical datasets each including radiological data of other patients are provided. Each case dataset is evaluated according to a predefined AI-based method to obtain a number of definitive features for that case dataset. The definitive features of the current case dataset are compared with the definitive features of each pre-stored medical dataset to identify a number of pre-stored medical datasets most similar to the current case dataset. The identified number of most similar pre-stored medical datasets are output.

Method and arrangement for identifying similar pre-stored medical datasets

Risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient are predicted. Data associated with a region of interest comprising the target region is received. The received data includes a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. A trained machine-learning algorithm is applied to the received data. The trained machine-learning algorithm generates at least one toxicity indicator based on the received data. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity.

Radiation-induced toxicity and machine learning

Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

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Skillman, NJ, United States of America

Ali Kamen