Increasing Trust in AI Using Explainable Artificial Intelligence for Histopathology - An Overview

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Abstract

Digital Pathology is an area that could benefit a lot from the automatic classification of scanned microscopic slides. One of the main problems with this is that the experts need to understand and trust the decisions of the system. This paper is an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts and ML engineers that work with histopathological images. This paper is an overview of the current state of the art methods used in the histopathological practice for explain. The search was performed using SCOPUS database and revealed that there are few applications of CNNs for digital pathology. The 4-term search yielded 99 results. This research sheds light on the main methods that can be used for histopathology classification and offers a good starting point for future works.

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Pesecan, C. M., & Stoicu-Tivadar, L. (2023). Increasing Trust in AI Using Explainable Artificial Intelligence for Histopathology - An Overview. In Studies in Health Technology and Informatics (Vol. 305, pp. 14–17). IOS Press BV. https://doi.org/10.3233/SHTI230411

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