PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 13 Jul 2023.
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9 of 32 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
- 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 387
- 2021Reducing Label Effort: Self-Supervised meets Active Learningcited by 42
- 2018Iterative annotation to ease neural network training: Specialized machine learning in medical image analysiscited by 21
- 2021HistoCartography: A Toolkit for Graph Analytics in Digital Pathologycited by 20
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