公开(公告)号：US11410306B2

公开(公告)日：2022-08-09

申请号：US17078878

申请日：2020-10-23

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Xinghui Fu ,  Zhongqian Sun ,  Hong Shang ,  Wei Yang

IPC: G06T7/00 ,  G06T7/11 ,  G06N3/08

Abstract: The present disclosure describes a method, an apparatus, and storage medium for recognizing medical image. The method includes obtaining, by a device, a medical image. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes determining, by the device, the medical image through a first recognition model to generate a lesion recognition result used for indicating whether the medical image comprises a lesion; and in response to the lesion recognition result indicating that the medical image comprises a lesion, recognizing, by the device, the medical image through a second recognition model to generate a lesion degree recognition result of the medical image used for indicating a degree of the lesion. Manual analysis and customization of a feature extraction solution are not required, so that the efficiency and accuracy of medical image recognition are improved.

公开(公告)号：US20210272681A1

公开(公告)日：2021-09-02

申请号：US17321219

申请日：2021-05-14

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Han ZHENG ,  Zhongqian Sun ,  Hong Shang ,  Xinghui Fu ,  Wei Yang

IPC: G16H30/40 ,  G16H50/20 ,  G06T7/00

Abstract: This application relates to an image recognition model training method, an image recognition method, apparatus, and system. The method includes: obtaining a to-be-recognized image; extracting image feature information of the to-be-recognized image; and obtaining a lesion category recognition result of the to-be-recognized image by using the image feature information of the to-be-recognized image as an input parameter of a preset image recognition model, the image recognition model being trained by using a training image sample set comprising at least one strong-label training image sample, to determine the lesion category recognition result; and the strong-label training image sample representing an image sample having strong-label information, and the strong-label information comprising at least annotation information of a lesion category and a lesion position in the strong-label training image sample. According to the lesion position, image feature information of a specific lesion category may be more accurately positioned, thereby improving reliability and accuracy.