• Engineering team develops new AI algorit

    From ScienceDaily@1:317/3 to All on Tue Apr 12 22:30:44 2022
    Engineering team develops new AI algorithms for high accuracy and cost effective medical image diagnostics

    Date:
    April 12, 2022
    Source:
    The University of Hong Kong
    Summary:
    Medical imaging is an important part of modern healthcare, enhancing
    both the precision, reliability and development of treatment for
    various diseases. Artificial intelligence has also been widely
    used to further enhance the process. However, conventional medical
    image diagnosis employing AI algorithms require large amounts of
    annotations as supervision signals for model training. To acquire
    accurate labels for the AI algorithms -- radiologists, as part of
    the clinical routine, prepare radiology reports for each of their
    patients, followed by annotation staff extracting and confirming
    structured labels from those reports using human-defined rules
    and existing natural language processing (NLP) tools. The ultimate
    accuracy of extracted labels hinges on the quality of human work
    and various NLP tools. The method comes at a heavy price, being
    both labour intensive and time consuming. An engineering team has
    now developed a new approach which can cut human cost down by 90%,
    by enabling the automatic acquisition of supervision signals from
    hundreds of thousands of radiology reports at the same time.

    It attains a high accuracy in predictions, surpassing its
    counterpart of conventional medical image diagnosis employing
    AI algorithms.



    FULL STORY ========================================================================== Medical imaging is an important part of modern healthcare, enhancing
    both the precision, reliability and development of treatment for various diseases.

    Artificial intelligence has also been widely used to further enhance
    the process.


    ========================================================================== However, conventional medical image diagnosis employing AI algorithms
    require large amounts of annotations as supervision signals for
    model training. To acquire accurate labels for the AI algorithms -- radiologists, as part of the clinical routine, prepare radiology reports
    for each of their patients, followed by annotation staff extracting
    and confirming structured labels from those reports using human-defined
    rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and
    various NLP tools. The method comes at a heavy price, being both labour intensive and time consuming.

    An engineering team at the University of Hong Kong (HKU) has developed a
    new approach "REFERS" (Reviewing Free-text Reports for Supervision), which
    can cut human cost down by 90%, by enabling the automatic acquisition
    of supervision signals from hundreds of thousands of radiology reports
    at the same time. It attains a high accuracy in predictions, surpassing
    its counterpart of conventional medical image diagnosis employing AI algorithms.

    The innovative approach marks a solid step towards realizing generalized medical artificial intelligence. The breakthrough was published in
    Nature Machine Intelligence in the paper titled "Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports." "AI-enabled medical image diagnosis has the potential
    to support medical specialists in reducing their workload and improving
    the diagnostic efficiency and accuracy, including but not limited to
    reducing the diagnosis time and detecting subtle disease patterns,"
    said Professor YU Yizhou, leader of the team from HKU's Department of
    Computer Science under the Faculty of Engineering.

    "We believe abstract and complex logical reasoning sentences in radiology reports provide sufficient information for learning easily transferable
    visual features. With appropriate training, REFERS directly learns
    radiograph representations from free-text reports without the need to
    involve manpower in labelling." Professor Yu remarked.

    For training REFERS, the research team uses a public database with
    370,000 X- Ray images, and associated radiology reports, on 14 common
    chest diseases including atelectasis, cardiomegaly, pleural effusion,
    pneumonia and pneumothorax. The researchers managed to build a radiograph recognition model using 100 radiographs only, and attains 83% accuracy
    in predictions. When the number was increased to 1,000, their model
    exhibits amazing performance with an accuracy of 88.2%, which surpasses
    its counterpart trained with 10,000 radiologist annotations (accuracy at 87.6%). When 10,000 radiographs were used, the accuracy is at 90.1%. In general, an accuracy above 85% in predictions is useful in real-world
    clinical applications.

    REFERS achieves the goal by accomplishing two report-related tasks,
    i.e., report generation and radiograph-report matching. In the first
    task, REFERS translates radiographs into text reports by first encoding radiographs into an intermediate representation, which is then used to
    predict text reports via a decoder network. A cost function is defined
    to measure the similarity between predicted and real report texts,
    based on which gradient-based optimization is employed to train the
    neural network and update its weights.

    As for the second task, REFERS first encodes both radiographs and
    free-text reports into the same semantic space, where representations of
    each report and its associated radiographs are aligned via contrastive learning.

    "Compared to conventional methods that heavily rely on human
    annotations, REFERS has the ability to acquire supervision from each
    word in the radiology reports. We can substantially reduce the amount
    of data annotation by 90% and the cost to build medical artificial intelligence. It marks a significant step towards realizing generalized
    medical artificial intelligence, " said the paper's first author Dr
    ZHOU Hong-Yu.


    ========================================================================== Story Source: Materials provided by The_University_of_Hong_Kong. Note:
    Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Hong-Yu Zhou, Xiaoyu Chen, Yinghao Zhang, Ruibang Luo, Liansheng
    Wang,
    Yizhou Yu. Generalized radiograph representation learning via
    cross- supervision between images and free-text radiology
    reports. Nature Machine Intelligence, 2022; 4 (1): 32 DOI:
    10.1038/s42256-021-00425-9 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220412095357.htm

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