학술논문
PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling
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Working Paper
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Abstract
The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.
Comment: The submission includes 15 pages, 8 figures, 1 table, and 30 references. It is a new submission
Comment: The submission includes 15 pages, 8 figures, 1 table, and 30 references. It is a new submission