公开(公告)号：US20190050982A1

公开(公告)日：2019-02-14

申请号：US16028389

申请日：2018-07-05

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Qi Song ,  Shanhui Sun ,  Feng Gao ,  Junjie Bai ,  Hanbo Chen ,  Youbing Yin

IPC: G06T7/00 ,  G06T7/70

Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.

公开(公告)号：US11341631B2

公开(公告)日：2022-05-24

申请号：US16028389

申请日：2018-07-05

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Qi Song ,  Shanhui Sun ,  Feng Gao ,  Junjie Bai ,  Hanbo Chen ,  Youbing Yin

IPC: G06T7/00 ,  G06T7/70 ,  G06K9/62 ,  G06V10/44

Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.

公开(公告)号：US20190114770A1

公开(公告)日：2019-04-18

申请号：US16049809

申请日：2018-07-31

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Qi Song ,  Bin Kong ,  Shanhui Sun

IPC: G06T7/00 ,  G06K9/62

Abstract: Embodiments of the disclosure provide systems and methods for detecting cancer metastasis in a whole-slide image. The system may include a communication interface configured to receive the whole-slide image and a learning model. The whole-slide image is acquired by an image acquisition device. The system may also include a memory configured to store a plurality of tiles derived from the whole-slide image in a queue. The system may further include at least one processor, configured to apply the learning model to at least two tiles stored in the queue in parallel to obtain detection maps each corresponding to a tile, and detect the cancer metastasis based on the detection maps.

公开(公告)号：US20190050981A1

公开(公告)日：2019-02-14

申请号：US15996434

申请日：2018-06-02

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Qi Song ,  Shanhui Sun ,  Hanbo Chen ,  Junjie Bai ,  Feng Gao ,  Youbing Yin

IPC: G06T7/00 ,  G06T7/11 ,  G06N3/04 ,  G06N5/04 ,  G06N99/00 ,  G06T11/20 ,  G16H30/40

Abstract: A computer-implemented method for automatically detecting a target object from a 3D image is disclosed. The method may include receiving the 3D image acquired by an imaging device. The method may further include detecting, by a processor, a plurality of bounding boxes as containing the target object using a 3D learning network. The learning network may be trained to generate a plurality of feature maps of varying scales based on the 3D image. The method may also include determining, by the processor, a set of parameters identifying each detected bounding box using the 3D learning network, and locating, by the processor, the target object based on the set of parameters.