Walker, Cédric AndréCédric AndréWalkerTalawalla, TasneemTasneemTalawallaToth, RobertRobertTothAmbekar, AkhilAkhilAmbekarRea, KienKienReaChamian, OswinOswinChamianFan, FanFanFanBerezowska, SabinaSabinaBerezowskaRottenberg, SvenSvenRottenberg0000-0003-2044-9844Madabhushi, AnantAnantMadabhushiMaillard, MarieMarieMaillardBarisoni, LauraLauraBarisoniHorlings, Hugo MarkHugo MarkHorlingsJanowczyk, AndrewAndrewJanowczyk2024-10-262024-10-262024-06-20https://boris-portal.unibe.ch/handle/20.500.12422/178292The 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.en600 - Technology::610 - Medicine & health600 - Technology::630 - Agriculturearticle10.48350/1979813890233610.1038/s41746-024-01150-4