Browsing by Subject "Medical shift data"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation
    (IEEE, 2023) Guo, Xiaoyuan; Wawira Gichoya, Judy; Trivedi, Hari; Purkayastha, Saptarshi; Banerjee, Imon; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Automated curation of noisy external data in the medical domain has long been demanding as AI technologies should be validated on various sources with clean annotated data. To curate a high-quality dataset, identifying variance between the internal and external sources is a fundamental step as the data distributions from different sources can vary significantly and subsequently affect the performance of the AI models. Primary challenges for detecting data shifts are – (1) access to private data across healthcare institutions for manual detection, and (2) the lack of automated approaches to learn efficient shift-data representation without training samples. To overcome the problems, we propose an automated pipeline called MedShift to detect the top-level shift samples and evaluating the significance of shift data without sharing data between the internal and external organizations. MedShift employs unsupervised anomaly detectors to learn the internal distribution and identify samples showing significant shiftness for external datasets, and compared their performance. To quantify the effects of detected shift data, we train a multi-class classifier that learns internal domain knowledge and evaluating the classification performance for each class in external domains after dropping the shift data. We also propose a data quality metric to quantify the dissimilarity between the internal and external datasets. We verify the efficacy of MedShift with musculoskeletal radiographs (MURA) and chest X-rays datasets from more than one external source. Experiments show our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently. The code can be found at https://github.com/XiaoyuanGuo/MedShift. An interface introduction video to visualize our results is available at https://youtu.be/V3BF0P1sxQE.
  • Thumbnail Image
    Item
    MedShift: identifying shift data for medical dataset curation
    (2021) Guo, Xiaoyuan; Gichoya, Judy Wawira; Trivedi, Hari; Purkayastha, Saptarshi; Banerjee, Imon; BioHealth Informatics, School of Informatics and Computing
    To curate a high-quality dataset, identifying data variance between the internal and external sources is a fundamental and crucial step. However, methods to detect shift or variance in data have not been significantly researched. Challenges to this are the lack of effective approaches to learn dense representation of a dataset and difficulties of sharing private data across medical institutions. To overcome the problems, we propose a unified pipeline called MedShift to detect the top-level shift samples and thus facilitate the medical curation. Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way. Second, without exchanging data across sources, we run the trained anomaly detectors on an external dataset B for each class. The data samples with high anomaly scores are identified as shift data. To quantify the shiftness of the external dataset, we cluster B's data into groups class-wise based on the obtained scores. We then train a multi-class classifier on A and measure the shiftness with the classifier's performance variance on B by gradually dropping the group with the largest anomaly score for each class. Additionally, we adapt a dataset quality metric to help inspect the distribution differences for multiple medical sources. We verify the efficacy of MedShift with musculoskeletal radiographs (MURA) and chest X-rays datasets from more than one external source. Experiments show our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently. An interface introduction video to visualize our results is available at https://youtu.be/V3BF0P1sxQE.