公开(公告)号：US20200265944A1

公开(公告)日：2020-08-20

申请号：US16652084

申请日：2018-10-03

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Vijayananda JAGANNATHA ,  Vinay PANDIT ,  Srinivas Prasad MADAPUSI RAGHAVAN ,  Rupesh VAKKACHI KANDI

IPC: G16H30/40 ,  G06F16/22 ,  G06F11/36 ,  G06Q10/06 ,  G06N7/00 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods are disclosed for generating device test cases for medical imaging devices, which are not only reflective of current actual field usage of the device but also provide outlook on future usage. A probabilistic model of usage patterns is generated from historic data present in the device field logs by mining the current usage patterns. A Deep Long Short Term Memory Neural Network model of the usage patterns is constructed to predict the future usage patterns. Additionally, to capture the changing trends of device usage patterns in the field, predictive models are continuously updated in real time, and the test cases generated log files by the models are integrated into an automated testing system.

公开(公告)号：US20230334656A1

公开(公告)日：2023-10-19

申请号：US17922809

申请日：2021-05-03

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Vidya Madapusi Srinivas PRASAD ,  Srinivasa Rao KUNDETI ,  Manikanda Krishnan V ,  Vijayananda JAGANNATHA

IPC: G06T7/00 ,  G06T3/40 ,  G06V10/44 ,  G06V10/42 ,  G06V10/82 ,  G06V10/774 ,  G06V10/764 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06T3/4053 ,  G06V10/44 ,  G06V10/42 ,  G06V10/82 ,  G06V10/774 ,  G06V10/764 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/10116 ,  G06T2207/30004 ,  G06V2201/031

Abstract: Disclosed herein is a method and system for identifying abnormal images in a set of medical images for optimal assessment of the medical images. A plurality of global features from each medical image is extracted based on pretrained weights associated with each global feature. Similarly, plurality of local features from each medical image is extracted analyzing a predefined number of image patches generated from a higher resolution image corresponding to each medical image. Further, an abnormality score for each medical image is determined based on weights associated with a combined feature set obtained by concatenating the plurality of global features and the plurality of local features. Thereafter, the medical image is identified as an abnormal image when the abnormality score of the medical image is higher than a predefined first threshold score.