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    APPARATUS FOR POLISHING
    8.
    发明公开
    APPARATUS FOR POLISHING 审中-公开

    公开(公告)号：EP4324595A2

    公开(公告)日：2024-02-21

    申请号：EP23212644.1

    申请日：2019-10-16

    申请人： Luben Glass S.r.l.

    发明人： BASSI, Erik

    IPC分类号： B24B41/06

    摘要： The present disclosure illustrates various apparatuses for polishing surfaces of mechanical pieces, each comprising a supporting frame, a suspension system mounted above the supporting frame, at least one vibrating table supported by the suspensions, at least one housing supported by the vibrating table for a container suitable for containing mechanical pieces to be polished or being itself a hollow mechanical piece to be internally polished, a drive unit functionally coupled to the vibrating table to make it vibrate, a locking/releasing system configured to lock/release the container to the vibrating table, as well as a system loading/discharging a polishing material. In the polishing equipment there is at least a dedicated system for a respective vibrating table of the apparatus, in which the dedicated system can be the suspension system, the loading/discharging system, the drive unit, or the locking/releasing system.

    BREAST ULTRASOUND DIAGNOSIS METHOD AND SYSTEM USING WEAKLY SUPERVISED DEEP-LEARNING ARTIFICIAL INTELLIGENCE
    10.
    发明公开
    BREAST ULTRASOUND DIAGNOSIS METHOD AND SYSTEM USING WEAKLY SUPERVISED DEEP-LEARNING ARTIFICIAL INTELLIGENCE 审中-公开

    公开(公告)号：EP4292538A1

    公开(公告)日：2023-12-20

    申请号：EP22752993.0

    申请日：2022-02-10

    申请人： Beamworks Corp.

    发明人： KIM, Jae Il ,  KIM, Won Hwa ,  KIM, Hye Jung

    IPC分类号： A61B8/08 ,  G16H50/20 ,  G16H30/40 ,  G16H30/20 ,  G06N3/08 ,  G06N3/04

    摘要： A breast ultrasound diagnosis method using weakly supervised deep-learning artificial intelligence comprises: an ultrasound image preprocessing step of generating input data including only an image region necessary for learning, by deleting personal information about a patient from a breast ultrasound image; a deep-learning step of receiving the input data, obtaining a feature map from the received input data by using a convolutional neural network (CNN) and global average pooling (GAP), and carrying out re-learning; a differential diagnosis step of determining the input data as one of normal, benign, and malignant by using the GAP, and when the input data is determined to be malignant, calculating a probability of malignancy (POM) indicating accuracy of the determination; and a contribution region determination and visualization step of backpropagating a determination result through the CNN, calculating a contribution degree of each pixel that has contributed to the determination result as a gradient and a feature value, and visualizing a contribution region that has contributed to the determination, together with the POM, on the basis of the calculated contribution degree of each pixel, wherein, in the deep-learning step, learning is carried out on the basis of verified performance of the contribution region and the POM.