MEDICAL IMAGE DIAGNOSIS APPARATUS AND MEDICAL IMAGE DIAGNOSIS SYSTEM

    公开(公告)号:US20200163550A1

    公开(公告)日:2020-05-28

    申请号:US16275773

    申请日:2019-02-14

    Abstract: A medical image diagnosis apparatus according to an embodiment includes an imager, a storage, and processing circuitry. The storage stores therein a correspondence relationship between each of disease patterns and scans used for performing a differential diagnosis process on the disease pattern. The processing circuitry obtains, based on image data acquired by a first scan, a disease pattern having a possibility of being applicable to the subject and a first index value indicating a degree of applicability of the disease pattern. The processing circuitry outputs information indicating the disease pattern to a display when it is possible to perform the differential diagnosis process based on the first index value and outputs an imaging condition used for performing a second scan based on the correspondence relationship and the disease pattern having the possibility when it is not possible to perform the differential diagnosis process based on the first index value.

    MEDICAL IMAGE PROCESSING METHOD, MEDICAL IMAGE PROCESSING APPARATUS, AND STORAGE MEDIUM

    公开(公告)号:US20240347175A1

    公开(公告)日:2024-10-17

    申请号:US18633744

    申请日:2024-04-12

    CPC classification number: G16H30/40 G06V10/26 G06V10/72 G06V10/7753 G06V10/82

    Abstract: A medical image processing method according to an embodiment of the present disclosure includes: training a deep neural network by using labeled image data; obtaining a first augmented image by carrying out a weak data augmentation on unlabeled image data; performing a predicting process on the first augmented image by using the deep neural network and determining whether each of the pixels in the first augmented image is able to serve as a pseudo-label on the basis of prediction information of the pixel; obtaining a second augmented image by carrying out a strong data augmentation on the first augmented image; training the deep neural network by using the second augmented image and the pseudo-labels; and updating the deep neural network on the basis of training results of the labeled image data and the unlabeled image data and processing a medical image by using the updated deep neural network.

    NUCLEIC ACID DETECTION SYSTEM AND NUCLEIC ACID DETECTION METHOD

    公开(公告)号:US20220127685A1

    公开(公告)日:2022-04-28

    申请号:US17506804

    申请日:2021-10-21

    Abstract: A nucleic acid detection system according to an embodiment is a nucleic acid detection system to detect a target nucleic acid in a sample and includes a thermal inactivation chamber, an amplification chamber, and a detection chamber, all of which constitute a liquid flow path. In this system, liquid flows through the thermal inactivation chamber, the amplification chamber, and the detection chamber sequentially. The thermal inactivation chamber includes a reagent to thermally inactivate and decompose the sample. The amplification chamber includes a reagent to amplify the target nucleic acid. The detection chamber includes a test strip to conduct a Cas enzyme reaction with the target nucleic acid and a lateral flow detection.

    IMAGE PROCESSING METHOD, IMAGE PROCESSING SYSTEM, AND NON-TRANSITORY COMPUTER READABLE MEDIUM

    公开(公告)号:US20230394791A1

    公开(公告)日:2023-12-07

    申请号:US18329832

    申请日:2023-06-06

    CPC classification number: G06V10/764 G06T7/11 G06V10/25

    Abstract: An image processing method according to an embodiment includes a specifying step, an inference step, and an integration step. In the specifying step, a first portion including a region corresponding to an anatomical site of a target and a second portion including a region different from the anatomical site are specified in the image. In the inference step, by using a deep learning model, segmentation of the region corresponding to the anatomical site is performed on the first portion and segmentation of the region different from the anatomical site is performed on the second portion, or classification and detection of an image including the region corresponding to the anatomical site is performed on the first portion and classification and detection of an image including the region different from the anatomical site is performed on the second portion. In the integration step, results of the respective processes are integrated for output.

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