Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology

By Andreas Holzinger, Bernd Malle, Peter Kieseberg, Peter M. Roth, Heimo Müller, Robert Reihs, Kurt Zatloukal. 2017

99 citations recorded by the source corpus.

Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *omics data) together with current advances in artificial intelligence/machine learning enable to make novel information accessible and quantifiable to a human expert, which is not yet available and not exploited in current medical settings. The grand goal is to reach a level of usable intelligence to understand the data in the context of an application task, thereby making machine decisions transparent, interpretable and explainable. The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points. Interestingly, humans can learn from such few examples and are able to instantly interpret complex patterns. Consequently, the grand goal is to combine the possibilities of artificial intelligence with human intelligence and to find a well-suited balance between them to enable what neither of them could do on their own. This can raise the quality of education, diagnosis, prognosis and prediction of cancer and other diseases. In this paper we describe some (incomplete) research issues which we believe should be addressed in an integrated and concerted effort for paving the way towards the augmented pathologist.

Subfields: Artificial Intelligence, Health Informatics

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, methodology): “Ante-hoc systems are interpretable by design towards glass-box approaches (Holzinger et al., 2017c); typical examples include linear regression, decision trees and fuzzy inference systems.”
Breast cancer histopathological image classification using a hybrid deep neural network. (2020, methodology): “A CNN is also used to extract the feature representation vector of each patch, and then the traditional machine learning classification algo-rithm, such as support vector machine (SVM), is used to make the classification result of the whole histopathological image [5].”
Explainable Agents and Robots: Results from a Systematic Literature Review (2019, background): “For this reason, this branch of XAI is interested in comprehending how the available data led to a decision, and whether, given the data and specific circumstances, the machine learning mechanism can be remodeled to output the same decision [50].”
Artificial intelligence for digital and computational pathology (2023, background): “The methodological advances in computational pathology (CPath) have greatly benefited from initiatives to create public datasets, in the form of either challenges or open data banks.”
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis (2019, background): “(2018), Holzinger et al. (2017)] for further reading on interpretability in the rest of the medical imaging domain.”