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Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Histopathology Procedures: From Tissue Sampling to Histopathological Evaluation

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Computational Nuclei Segmentation Methods in Digital Pathology: A Survey

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Inter-observer agreement among pathologists in grading the pathological response to neoadjuvant chemotherapy in breast cancer

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Multifaceted fused-CNN based scoring of breast cancer whole-slide histopathology images

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Artificial intelligence in digital breast pathology: Techniques and applications

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Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma

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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

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Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

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NuClick: A deep learning framework for interactive segmentation of microscopic images

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Deep neural network models for computational histopathology: A survey

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Automated quality assessment of large digitised histology cohorts by artificial intelligence

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Collaborative analysis of multi-gigapixel imaging data using Cytomine

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Robust Histopathology Image Analysis: To Label or to Synthesize?

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2013 IEEE Conference on Computer Vision and Pattern Recognition

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The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis

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Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

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pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long‐term follow‐up

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The National Center for Biomedical Ontology

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Standardized Clinical Annotation of Digital Histopathology Slides at the Point of Diagnosis

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