PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for
  Object Labeling
Cedric Walker Tasneem Talawalla Robert Toth Akhil Ambekar Kien Rea Oswin Chamian Fan Fan Sabina Berezowska Sven Rottenberg Anant Madabhushi Marie Maillard Laura Barisoni Hugo Mark Horlings Andrew Janowczyk
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.