ArticleThe journal of pathology. Clinical research2022
Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations.
Article in The journal of pathology. Clinical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review.Journal of medical Internet research · 2026Article
- Fusion Annotator: A Platform for Accelerating Consensus-Driven Ground Truth Generation with AI Assistance.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0.Journal of imaging informatics in medicine · 2026Article
- Article
- An open-source platform for structured annotation and computational workflows in digital pathology research.Scientific reports · 2025Article
- Digital Pathology and Artificial Intelligence Applied to Nonclinical Toxicology Pathology-The Current State, Challenges, and Future Directions.Toxicologic pathology · 2025Review
- Review
- Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.Journal of imaging informatics in medicine · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence.Cancers · 2024Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.Cancers · 2024Review
- Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- Closing the loop - the role of pathologists in digital and computational pathology research.The journal of pathology. Clinical research · 2024Article
- AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.NPJ precision oncology · 2023Article
- Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.British journal of cancer · 2023Article
- Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast CancerThe Journal of pathology · 2023Review
- Unleashing the potential of AI for pathology: challenges and recommendations.The Journal of pathology · 2023Review
- Improving Representation Learning for Histopathologic Images with Cluster Constraints.Proceedings. IEEE International Conference on Computer Vision · 2023Article
- BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images.Database : the journal of biological databases and curation · 2022Article
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in whole-slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence-based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue, and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms that are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well-defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large-scale annotation exercise involving a multidisciplinary team of pathologists, ML experts, and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real-world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary, and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
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