ArticleBioinformatics (Oxford, England)2019
Structured crowdsourcing enables convolutional segmentation of histology images.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 2 of them syntheses that pooled it.
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Who cites it
89 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review.BMC cancer · 2024Pooled it
- Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.The Journal of pathology · 2019Guideline
- Tumor-Aware Unsupervised Digital HER2 Immunohistochemistry via Dual Contrastive Learning.Biosensors · 2026Article
- Nationwide federated learning for histopathology: secure deployment across Germany behind firewalls.NPJ digital medicine · 2026Article
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.Research square · 2026Article
- Gompertz growth with a shared carrying capacity optimally simulates primary and metastatic tumor growth dynamics.British journal of cancer · 2026Article
- Convpaint-Interactive pixel classification using pretrained neural networks.Cell reports methods · 2026Article
- Label Noise in Pathological Segmentation Is Overlooked, Leading to Potential Overestimation of Artificial Intelligence.Cancer science · 2026Article
- Tumor microenvironment transcriptional activity enables robust stratification of chemotherapy response in triple-negative breast cancer.Cell reports. Medicine · 2026Article
- Enhanced colorectal gland segmentation through multi-scale attention and contextual feature fusion.Scientific reports · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Histological Hyperspectral Breast Cancer Recurrence Database (HistologyHSI-BC Recurrence).Scientific data · 2025Article
- Histopathology-based Spatial Profiling of Immune and Molecular Features Predicts Cancer risk in Barrett's Esophagus.medRxiv : the preprint server for health sciences · 2025Article
- Web engine for tumor pathology image retrievals on massive scales.bioRxiv : the preprint server for biology · 2025Article
- CrowdAttention: An Attention Based Framework to Classify Crowdsourced Data in Medical Scenarios.Sensors (Basel, Switzerland) · 2025Article
- Spatial gene expression at single-cell resolution from histology using deep learning with GHIST.Nature methods · 2025Article
- Article
- Supporting intraoperative margin assessment using deep learning for automatic tumour segmentation in breast lumpectomy micro-PET-CT.NPJ breast cancer · 2025Article
- Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction.Journal for immunotherapy of cancer · 2025Review
29 more citing papers are in PubMed but not listed here.
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Authors and funding
31 authors.
Funding
Abstract
motivationWhile deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.
resultsWe recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.