DATA DISCRIMINATION IN PATHOMORPHOLOGY. WAYS OF COPING
DOI:
https://doi.org/10.11603/mie.1996-1960.2022.3.13359Keywords:
data discrimination, big data, data compression, virtual pathology slides, visual discrimination model, neural convolutional networks, 4S algorithmAbstract
Background. The issues of modern data analytics are considered, among which the risk of discrimination is the most acute. There are still no major studies on this topic in pathomorphology. Our work aims to identify the causes and consequences of discrimination in data mining and explore potential solutions to this problem. The purpose of the work was to identify the main reasons for the appearance of data discrimination in pathomorphology and conceptual substantiation of ways to overcome incomplete or biased data.
Materials and methods. Results. There are a large number of available compression schemes that help to solve various artifact problems (for example, blocking and color distortion, which is especially important for pathology, hot spot selection), but do not solve them all. Does not have a definitive solution to the effect of compressing the initial scan. To date, there is too little research on this topic.
Conclusions. A significant amount of work needs to be done before the complex relationships between the quality of the original sample, the quality of the scanned image, the quantitative characteristics of the applied compression and the impact on diagnostic interpretation and other related diagnostic procedures (structural classification, prognosis etc.). The proposed 4S algorithm (systematization, structuring stability of states), associated with the use of technology for creating stable morphological, histological, or other structures. The experience of its use provides a basis for cautious optimism.
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