Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Aic Innovations Group, Inc.

Isp Investments Inc.

Siemens Healthineers Ag

Siemens Corporate Research, Inc.

Siemens Corporation

Covera Health, Inc.

Li Zhang

Daguang Xu

Adam Hanina

Daniel Glasner

Tommaso Mansi

Benjamin L Odry

Carol L Novak

Ming Fang

Olga Dueva-Koganov

Julian Krebs

Qing Xu

Artyom Duev

Dorin Comaniciu

Bogdan Georgescu

Shaohua Kevin Zhou

Ali Kamen

Vivek Kumar Singh

Sasa Grbic

David D Liu

Lei Guan

Rui Liao

Shun P Miao

Christophe Chefd'hotel

Florin Cristian Ghesu

Vincent Ordy

Pierre De Tournemire

Yefeng Zheng

Michal Sofka

Bernhard Geiger

Michael Koganov

Noha Youssry El-Zehiry

Chris Schwemmer

Robert A Turner

Nathan S Lay

Hong Chen

Karine Cucumel

Steven Micceri

Tiantian Zhang

Paul Recht

Gilles Oberto

Philipp Wolber

Ulf Schäfer

Chong Chen

Mariappan S Nadar

Shanhui Sun

Marcel Dominik Nickel

Atilla Peter Kiraly

Jianzhong Qian

Guo-Qing Wei

Li Fan

Gregory G Slabaugh

Gozde Unal

Zhoubing Xu

Lutz Gündel

Christoph Guetter

Shang-Hong Lai

Hendrik Ditt

Ying Chi

Tongbai Meng

Richard J Herzog

Ryan Scott Bardsley

Nora Hünemohr

Bohong Zhang

James Robert Browning

Isaac Galatzer-Levy

Muhammad Anzar Abbas

Pavlo Dyban

Sajan Goud Lingala

Yiwen Wang

Jing Huo

Amer Abufadel

Vidya Koesmahargyo

Jayashri Pawar

Micha Kornreich

Aubrey Chow

Xi Cheng

Ozan Oktay

Kaifang Du

Tony Nguyen

Heinz Fichte

Guyu Li

Mathews Jacob

Chloe Chah

Anantha R Krishnan

Robert Turner

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

Systems and methods for determining a heath status of a patient through an automated interview. One of the systems include one or more computers in one or more locations and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations including: providing, to a user interface of a user device, questions for a user to respond to in an interactive manner, in which each of the questions following the first questions is adaptive based on the user's response to one or more of the previous questions; capturing motion and appearance of the user in a video sequence while the user is responding to the questions; and analyzing the motion of the user in the video sequence to determine one or more indications of a disease or of a change in a disease progression.

Method and apparatus for determining health status

Described are techniques for uncertainty-aware anatomical landmark detection, using, for example, a deep reinforcement learning (DRL) anatomical landmark detection agent. For instance, a process can include generating one or more image features for an input medical image using a first sub-network of the anatomical landmark detection agent. A softmax layer of a second sub-network of the anatomical landmark detection agent can generate a plurality of discrete Q-value distributions for a set of allowable actions associated with movement of the agent within the medical image. An anatomical landmark location within the medical image can be predicted using the discrete Q-value distributions. An uncertainty can be determined for the predicted anatomical landmark location, based on an average full width half maximum (FWHM) calculated for the plurality of discrete Q-value distributions.

Uncertainty-aware deep reinforcement learning for anatomical landmark detection in medical images

A deep machine-learning approach is used for medical image fusion by a medical imaging system. This one approach may be used for different applications. For a given application, the same deep learning is used but with different application-specific training data. The resulting deep-learnt classifier provides a reduced feature vector in response to input of intensities of one image and displacement vectors for patches of the one image relative to another image. The output feature vector is used to determine the deformation for medical image fusion.

Deep-learnt tissue deformation for medical imaging

Disclosed herein are methods of improving skin appearance associated with skin aging. The method comprises the step of applying an anti-aging bioactive composition to a skin surface having at least one sign of aging. The anti-aging bioactive composition is applied for a period of time sufficient to improve the appearance of the at least one sign of aging. In one embodiment, the anti-aging bioactive composition comprises an effective amount of a(Sacred Lotus) serum fraction. In another embodiment, the anti-aging bioactive composition comprises an effective amount of a(German Chamomile) Flower serum fraction. In other embodiments, the anti-aging bioactive composition can further include a dermatologically acceptable carrier.

Mitigating adverse effects of sunlight with ingredients obtained from living plants

A computer-implemented method includes obtaining a video of a subject, the video including a plurality of frames; generating, based on the plurality of frames, a plurality of optical flows; and encoding the plurality of optical flows using an autoencoder to obtain a movement-based biomarker value of the subject.

Biomarker determination using optical flows

A system and method for recognizing an object. The system includes an imaging apparatus for capturing an image of an object and a processor for receiving the captured image of the object, and for, when it is determined that fewer than a predetermined number of objects have been previously imaged, for determining whether the image of the captured object includes one or more characteristics determined to be similar to a same characteristic in a group of previously imaged objects so that the captured image is grouped with the previously imaged objects, or whether the image of the captured object includes one or more characteristics determined to be dissimilar to a same characteristic in a group of previously imaged objects so that the captured image is not grouped with the previously imaged objects, and the image of the captured object starts another group of previously imaged objects.

Apparatus and method for object recognition

Methods and systems for recognizing suspicious activities of a user administering a medication are described. One of the methods include capturing one or more video sequences of a user administering a medication by a video capture device; analyzing the captured one or more video sequences to determine one or more indications of a suspicious activity of the user; and adaptively performing a follow-up operation in response to a determination of one or more indications of a suspicious activity of the user.

Recognition of suspicious activities in medication administration

The present invention relates to bioactive compositions (including, for example, bioactive serum fractions and bioactive extracts) derived from ginseng (spp.) fresh whole plant, a particular part of the plant (e.g., rhizome, roots, leaves, stems, flowers, fruit) or any combination of these plant parts. The bioactive serum fractions and bioactive extracts are free of or substantially free of pulegone and/or free of or substantially free of proteins. The present invention also provides products comprising the bioactive serum fractions and/or bioactive extracts. The present invention further provides methods of making and using the bioactive serum fractions and/or bioactive extracts. Further, the bioactive serum fractions and bioactive extracts have anti-inflammatory and/or anti-irritant and/or anti-aging activities.

Bioactive compositions from ginseng plant (spp.) and methods for their production and use

The present invention provides bioactive serum fractions and bioactive extracts derived from an Achachairu () plant. The bioactive serum fractions and bioactive extracts are free of or substantially free of benzophenone and/or free of or substantially free of proteins. The present invention also provides products comprising the bioactive serum fractions and/or bioactive extracts. The present invention further provides methods of making the bioactive serum fractions and/or bioactive extracts.

Bioactive serum fraction from achachairu () which is free of benzophenone and proteins

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Skillman, NJ, United States of America

Li Zhang