Machine Learning Methods for Histopathological Image Analysis

Komura, Daisuke; Ishikawa, Shumpei

doi:10.1016/j.csbj.2018.01.001

Cited by 681 publications

(505 citation statements)

References 91 publications

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“…This reduces the number of samples to be used and the types of labelling (Gry et al ., ; Sobhani et al ., ; Laas et al ., ). In other cases, the immunohistochemical images are used to illustrate the morphological patterns characteristic of the expression of the molecular markers (Moro et al ., ). Enhancement of the immunohistochemistry imaging contrast by performing the PCA analysis on the RGB image taken with a colour CCD camera, together with automatic classification algorithms or Machine Learning (Loukas et al ., ; Khorshed, ; Sarnecki et al ., ; Komura & Ishikawa, ; Van Eycke et al ., ). The degree of marking of an immunohistochemical image is usually done through a subjective numerical ‘scoring’ that assigns a ‘score’ to each image. The PCA method is usually used on the data of these numerical values (Ocak et al ., ).…”

Section: Principal Component Analysis Methods Applied To Lens Imagesmentioning

confidence: 99%

“…Enhancement of the immunohistochemistry imaging contrast by performing the PCA analysis on the RGB image taken with a colour CCD camera, together with automatic classification algorithms or Machine Learning (Loukas et al ., ; Khorshed, ; Sarnecki et al ., ; Komura & Ishikawa, ; Van Eycke et al ., ).…”

Section: Principal Component Analysis Methods Applied To Lens Imagesmentioning

confidence: 99%

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Collagen IV and laminin‐1 expression in embryonic mouse lens using principal components analysis technique

Sijilmassi

Alonso²,

Asensio

et al. 2018

Journal of Microscopy

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Immunohistochemistry section staining is not always easy to interpret. Manual quantification of immunohistochemical staining is limited by the observer visual ability to detect changes in level staining. Hence, the quantification of immunostaining by means of digital image analysis allows us to measure accurately protein expression percentages in immunobiological stained tissues and ensures to overcome the visual limitations. We perform an experimental study to analyse the impact of folic acid (FA) deficiency into collagen IV and laminin-1 expression in the embryonic mouse lens. The study starts with microscope images of embryos mouse lens whose mothers fed a diet deficient in FA during 2 and 8 weeks. A principal component analysis (PCA) image processing is used to analyse these images coming from control and FA deficit groups. The method permits to define an index of over- or infraexpression of collagen IV and laminin-1 associated to different spatial organisation structures (PC processes). Additionally, it permits to determine in precise percentage the exact quantity of the overexpression or infraexpression and finally to comprehend molecular regionalisation and expression in both control and deficient groups. The results suggest that even with 2 weeks of deficit of FA the expression and distribution of both molecules is affected.

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Section: Principal Component Analysis Methods Applied To Lens Imagesmentioning

confidence: 99%

Section: Principal Component Analysis Methods Applied To Lens Imagesmentioning

confidence: 99%

Collagen IV and laminin‐1 expression in embryonic mouse lens using principal components analysis technique

Sijilmassi

Alonso²,

Asensio

et al. 2018

Journal of Microscopy

View full text Add to dashboard Cite

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“…Digital pathological images generally have some issues to be considered, including the very large image size (and the involved problems for DL), insufficiently labeled images (the small training data available), the time needed from the pathologist (expensive labeling), insufficient labels (region of interest), different levels of magnification (resulting in different levels of information), color variation and artifacts (sliced and placed on glass slides) etc. (Komura & Ishikawa, ).…”

Section: Future Outlookmentioning

confidence: 99%

Causability and explainability of artificial intelligence in medicine

Holzinger

Langs

Denk

et al. 2019

WIREs Data Min & Knowl

908

571

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Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black‐box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of explainable medicine we need causability. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations. In this article, we provide some necessary definitions to discriminate between explainability and causability as well as a use‐case of DL interpretation and of human explanation in histopathology. The main contribution of this article is the notion of causability, which is differentiated from explainability in that causability is a property of a person, while explainability is a property of a system This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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“…Her‐2 and Ki67 tools are already available, with many other markers in development), disease quantification, morphometrics, tumour detection and cancer grading, and rare event screening (e.g. highlighting samples where tumour or micrometastases are detected and need pathologist review, and those which are negative and may not need review) .…”

Section: Potential Applicationsmentioning

confidence: 99%

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

Colling

Pitman

Oien

et al. 2019

The Journal of Pathology

190

126

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The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology workforce shortages, there is a clear need for a robust and evidence‐based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM‐Path) initiative and the British In Vitro Diagnostics Association (BIVDA) have set out a roadmap to help academia, industry, and clinicians develop new software tools to the point of approved clinical use. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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Machine Learning Methods for Histopathological Image Analysis

Cited by 681 publications

References 91 publications

Collagen IV and laminin‐1 expression in embryonic mouse lens using principal components analysis technique

Collagen IV and laminin‐1 expression in embryonic mouse lens using principal components analysis technique

Causability and explainability of artificial intelligence in medicine

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

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