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.
View Article and Find Full Text PDFInhibition of angiogenesis is an important mode of action for the teratogenic effect of chemicals and drugs. There is a gap in the availability of simple, experimental screening models for the detection of angiogenesis inhibition. The zebrafish embryo represents an alternative test system which offers the complexity of developmental differentiation of an entire organism while allowing for small-scale and high-throughput screening.
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