# Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations

arXiv: https://arxiv.org/abs/2106.13689 · HTML: https://mlsanity.com/paper/2106.13689/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [Multi-Instance Multi-Scale CNN for Medical Image Classification](https://mlsanity.com/paper/1907.02413/) (2019)

## Built on by (most cited first, 13 of 13)

- [BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images](https://mlsanity.com/paper/2111.04740/) (2021)
- [One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification](https://mlsanity.com/paper/2203.00077/) (2022)
- [CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting](https://mlsanity.com/paper/2303.06274/) (2023)
- [Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images](https://mlsanity.com/paper/2202.07001/) (2022)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input](https://mlsanity.com/paper/2108.13368/) (2021)
- [Improving Representation Learning for Histopathologic Images with Cluster Constraints](https://mlsanity.com/paper/2310.12334/) (2023)
- [Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images](https://mlsanity.com/paper/2302.03014/) (2023)
- [SPLICE -- Streamlining Digital Pathology Image Processing](https://mlsanity.com/paper/2404.17704/) (2024)
- [Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits](https://mlsanity.com/paper/2112.09496/) (2021)
- [Beyond Occlusion: In Search for Near Real-Time Explainability of CNN-Based Prostate Cancer Classification](https://mlsanity.com/paper/2512.17416/) (2025)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)
- [Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0](https://mlsanity.com/paper/2505.14926/) (2025)

## Abstract (arXiv)

Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilize information embedded in pathology WSIs beyond what we obtain 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 which 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.