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  • Review Article 2019-06-30 2019-06-30 \ 0 \ 1287 \ 405

    Deep-Learning-Based Molecular Imaging Biomarkers: Toward Data-Driven Theranostics

    Hongyoon Choi

    https://doi.org/10.14316/pmp.2019.30.2.39

    Abstract

    Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

  • Review Article 2019-06-30 2019-06-30 \ 18 \ 10253 \ 798

    Medical Image Analysis Using Artificial Intelligence

    Hyun Jin Yoon1,2, Young Jin Jeong1,2, Hyun Kang2, Ji Eun Jeong1, Do-Young Kang1,2

    https://doi.org/10.14316/pmp.2019.30.2.49

    Abstract

    Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deeplearning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

  • Original Article 2019-06-30 2019-06-30 \ 0 \ 729 \ 307

    Development of a Web-Based Program for Cross- Calibration and Record Management of Radiation Measuring Equipment

    So Hyun Park1, Rena Lee2, Kyubo Kim2, Sohyun Ahn3, Sangwook Lim4, Samju Cho5

    https://doi.org/10.14316/pmp.2019.30.2.59

    Abstract

    Purpose

    To manage radiation measurement equipment, a web-based management program has been developed in this study.

    Materials and Methods

    This program is based on a web service and Java Server Pages (JSP) and employs compatibility and accessibility.

    Results

    The first step in the workflow has been designed to create accounts for each user or organization and to log in. The program consists of two parts: fields for listed instruments, and measurement information. The instruments for measuring radiation listed in this program are as follows: ionization chambers, survey meters, thermometers, barometers, electrometers, and phantoms. Instrument properties can be put in the recording fields and browsing for associated instruments can be performed. The main part of the program is the cross-calibration for each ion chamber. For instance, the ionization chamber to be used as a relative dosimeter can be registered by cross-calibration data with a reference chamber calibrated by an accredited laboratory. This program supports methods using the central axis transfer theory for cross-calibration for the ionization chambers. The reference and field ionization chambers were placed in a Solid WaterTM phantom along the beam central axis at two different depths, and then the positions were switched. Each measured value was used for calculating the cross-calibration factor.

    Conclusions

    Because many instruments are used and managed in radiation oncology departments, systematic, traceable recording is very important. The web-based program developed in this study is expected to be used effectively in the maintenance of radiation measurement instruments.

Korean Society of Medical Physics

Vol.35 No.2
2019-06-30

pISSN 2508-4445
eISSN 2508-4453
Formerly ISSN 1226-5829

Frequency: Quarterly

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