公开(公告)号：US20230005113A1

公开(公告)日：2023-01-05

申请号：US17741098

申请日：2022-05-10

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Guang LI ,  Jinchen LI ,  Chengwei SUN ,  Cong CHEN ,  Kunlin CAO ,  Qi SONG

IPC: G06T5/50 ,  G06T7/00 ,  G06T7/73 ,  G06T7/174

Abstract: A method for medical image data enhancement is provided. The method includes: receiving a medical image sample set related to an object to be detected; based on an attribute of the object lacking in the medical image sample set, selecting a first medical image and a second medical image from the medical image sample set, where the first medical image contains the object lacking the attribute, and the second medical image does not contain the object lacking the attribute; determining a first area image block containing the lacking attribute; determining a second area image block not containing the lacking attribute; generating a composite area image block by fusing the first area image block and the second area image block based on a mask including an object part and a peripheral part around the object part; embedding the composite area image block back into the second medical image to obtain a third medical image; including the third medical image in the medical image sample set to obtain a data-enhanced medical image sample set.

公开(公告)号：US20220222812A1

公开(公告)日：2022-07-14

申请号：US17482901

申请日：2021-09-23

Applicant: Shenzhen Keya Medical Technology Corporation

Inventor： Jinchen LI ,  Guang LI ,  Chengwei SUN ,  Kunlin CAO ,  Qi SONG

IPC: G06T7/00 ,  G16H30/40 ,  G06K9/62 ,  G06N3/04

Abstract: The present disclosure provides a method, a device, and a non-transitory computer-readable storage medium for detecting a medical condition of an organ. The method includes obtaining 2D image sequences of the organ in a plurality of different directions and applying a plurality of classification branches to the 2D image sequences. Each classification branch receives a 2D image sequence of one direction and provides a classification result with respect to that direction. Each classification branch includes a convolutional neural network configured to extract first image features from the corresponding 2D image sequence and a recurrent neural network configured to extract second image features from the first image features. The method further includes fusing the classification results provided by the plurality of classification branches for detecting the medical condition.