Open Practices and Resources for Collaborative Digital Pathology

Marée, Raphaël

doi:10.3389/fmed.2019.00255

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Without full whole‐slide or AI support, these have limited use nowadays, but were nevertheless important in demonstrating the value of open methods being available for pathologists. More recently, several new open‐software applications have been developed with whole‐slide image analysis as a primary goal [89], including TMarker [90], Orbit [91], and QuPath [13] (desktop applications), and Cytomine [92] and the Digital Slide Archive [93] (web‐based platforms).…”

Section: Implementation Accessibility and Opennessmentioning

confidence: 99%

Developing image analysis methods for digital pathology

Bankhead

2022

The Journal of Pathology

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The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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Section: Implementation Accessibility and Opennessmentioning

confidence: 99%

Developing image analysis methods for digital pathology

Bankhead

2022

The Journal of Pathology

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“…1 Düzgün ruhsatlandırılmamış ve kanıta dayalı doğrulama yapılmamış araçların kullanıma girmesi, akademik taraflarla sistemi geliştirecek endüstri taraflarının iş birliği yapmaya direnç göstermelerinin yanı sıra ticari entegrasyona ve kullanıma açık kaynak veri formatlarına ihtiyaç olması da dijital patolojinin önündeki önemli bariyerlerdir. 14,20,21 Günümüzde Amerika Birleşik Devletleri (ABD), Avrupa ve Uzak Doğu'da çeşitli merkezlerde dijital patoloji uygulaması yapılmaktadır. Birçok merkezde kurulmuş olan dijital patoloji sistemi hâlen, uzaktan konsültasyon, kalite kontrolü, araştırma ve eğitim gibi aktivitelerde kullanılmakta olup, rutin tanı ve prognoz tespiti için uygun olup olmadığı test edilmektedir.…”

Section: Di̇ji̇tal Patoloji̇ni̇n öNemi̇unclassified

Digital Era and Artificial Intelligence in Pathology: Basic Information

Sensu¹,

Erdoğan²,

Gürbüz³

2020

Turkiye Klinikleri J Med Sci

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ÖZET Modern bilimsel laboratuvar ortamında dijital patoloji giderek önem kazanmakta ve artan bir teknolojik gereksinim hâline gelmektedir. Şimdilik genellikle konsültasyon, eğitim ve araştırma amacıyla kullanılmakla birlikte, bazı merkezlerde rutin patoloji pratiğine girmeye de başlamıştır. İnsanlar tarafından makinelere kazandırılan, deneyimleri hatırlama, bunlardan öğrenme, düşünme, yargılama ve karar verme becerisi olarak tanımlanan yapay zekâ uygulamalarının patolojide de çeşitli kolaylıklar sağlayacağı düşünülmektedir. Bu makalede, dijital patoloji ve patolojide yapay zekâ ile ilgili temel terim ve tanımlamalar tanıtılmakta; dijital patoloji laboratuvarı kurulurken göz önüne alınacak unsurlar anlatılmakta; dijital patoloji ve patolojide yapay zekâ uygulamalarının getireceği avantajlardan ve sorunlu yönlerden söz edilmekte ve dünyadaki uygulamalara yer verilmektedir.Anah tar Ke li me ler: Yapay zekâ; patoloji; telepatoloji; bilgisayarlar ABS TRACT Digital pathology has an increasing importance and is becoming a technological requirement for the modern scientific laboratory environment. Nowadays, it is generally used for consultation, education and research purposes. However, in certain pathology centers, it has also started to be used for primary routine practices. Artificial intelligence which is defined as the capability of a machine to learn, think and imitate human intelligence is expected to facilitate the burden of pathologists. In this review article, first, relevants terms and definitions on digital pathology and artificial intelligence as well as the factors to be taken into account for digitalization in pathology are described; then, related advantages and obstacles are mentioned and global experience is presented.Sibel ŞENSU ve ark.Tur ki ye Kli nik le ri J Med Sci. 2020;40(1):104-12

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“…Possibilities for such standardisation include using DICOM [19] or the OME format which supports structured annotations. Similarly, initiatives towards sharing of standards, data, and methods will pave the way to collaborative CPath [20].…”

Section: Introductionmentioning

confidence: 99%

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

Wahab

Miligy

Dodd

et al. 2022

The Journal of Pathology CR

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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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Open Practices and Resources for Collaborative Digital Pathology

Cited by 11 publications

References 27 publications

Developing image analysis methods for digital pathology

Developing image analysis methods for digital pathology

Digital Era and Artificial Intelligence in Pathology: Basic Information

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

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