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公开(公告)号：US20240288523A1
公开(公告)日：2024-08-29
申请号：US18570672
申请日：2022-06-22
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Sergey Kastryulin , Artyon Tsanda , Nicola Pezzotti
CPC classification number: G01R33/5608 , G01R33/4818 , G06T11/005 , G06T11/006 , G06T2210/41 , G06T2211/424 , G06T2211/441
Abstract: Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120). The medical system further comprises a computational system (104). Execution of the machine executable instructions causes the computational system to: reconstruct or receive (202) a test magnetic resonance image reconstructed from undersampled k-space data; receive (204) a test signal in response to inputting the test magnetic resonance image into an out of distribution testing neural network; and provide (206) the test signal. The test neural network is configured for outputting the test signal in response to receiving the test magnetic resonance image. The test signal is descriptive if the test magnetic resonance image is within a training distribution defined by a set of training data.
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公开(公告)号：US20240355094A1
公开(公告)日：2024-10-24
申请号：US18683595
申请日：2022-08-11
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Sergey Kastryulin , Alexey Chernyavskiy , Nicola Pezzotti
CPC classification number: G06V10/7715 , G06T11/005 , G06V10/235 , G06V10/464 , G06V10/774 , G06V10/993 , G06V2201/03
Abstract: Disclosed herein is a medical system (100) comprising a memory (110) storing machine executable instructions (120). The memory (110) further stores a trained first machine learning module (122) trained to output in response to receiving a medical image (124) as input a saliency map (126) as output. The saliency map (126) is predictive of a distribution of user attention over the medical image (124). The medical system (100) further comprises a computational system (104). Execution of the machine executable instructions (120) causes the computational system (104) to receive a medical image (124). The medical image (124) is provided as input to the trained first machine learning module (122). In response to the providing of the medical image (124), a saliency map (126) of the medical image (124) is received as output from the trained first machine learning module (122). The saliency map (126) predicts a distribution of user attention over the medical image (124). The saliency map (126) of the medical image (124) is provided.
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公开(公告)号：US20230394652A1
公开(公告)日：2023-12-07
申请号：US18031889
申请日：2021-10-11
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Nicola Pezzotti , Christian Wuelker , Tim Nielsen , Karsten Sommer , Michael Grass , Heinrich Schulz , Sergey Kastryulin
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06T2207/20084 , G06T2207/20081
Abstract: Disclosed herein is a medical system (100, 300, 400) comprising a memory (110) storing a trainable machine learning module (122) trained using training data descriptive of a training data distribution (600) to output a reconstructed medical image (136) in response to receiving measured medical image data (128) as input. The medical system comprises a computational system (104). The execution of machine executable instructions (120) causes the computational system to: receive (200) the measured medical image data and determine (202) the out-of-distribution score and the in-distribution accuracy score consecutively in an order determined a sequence, detect (204) a rejection of the measured medical image data using the out-of-distribution score and/or the in-distribution accuracy score during execution of the sequence, provide (206) a warning signal (134) if the rejection of the measured medical image data is detected. The out-of-distribution score is determined by inputting the measured medical image data into the out-of-distribution estimation module. The in-distribution accuracy score is determined by inputting the measured medical image data into the in-distribution accuracy estimation module.
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