  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.
