Open AccessSemantic annotation for computational pathology: multidisciplinary experience and best practice recommendations
Author(s) -
Wahab Noorul,
Miligy Islam M,
Dodd Katherine,
Sahota Harvir,
Toss Michael,
Lu Wenqi,
Jahanifar Mostafa,
Bilal Mohsin,
Graham Simon,
Park Young,
Hadjigeorghiou Giorgos,
Bhalerao Abhir,
Lashen Ayat G,
Ibrahim Asmaa Y,
Katayama Ayaka,
Ebili Henry O,
Parkin Matthew,
Sorell Tom,
Raza Shan E Ahmed,
Hero Emily,
Eldaly Hesham,
Tsang Yee Wah,
Gopalakrishnan Kishore,
Snead David,
Rakha Emad,
Rajpoot Nasir,
Minhas Fayyaz
Publication year - 2022
Publication title -
the journal of pathology: clinical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.849
H-Index - 21
ISSN - 2056-4538
DOI - 10.1002/cjp2.256
Subject(s) - annotation , computer science , multidisciplinary approach , best practice , data science , digital pathology , component (thermodynamics) , analytics , multidisciplinary team , artificial intelligence , information retrieval , medicine , social science , physics , management , sociology , economics , thermodynamics , nursing
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 Path ology image data L ake for A nalytics, K nowledge and E ducation (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.