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

arXiv: https://arxiv.org/abs/1712.06657 · HTML: https://mlsanity.com/paper/1712.06657/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 13 of 13)

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- [KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images](https://mlsanity.com/paper/2304.08240/) (2023)
- [Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems](https://mlsanity.com/paper/2307.03853/) (2023)
- [From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process](https://mlsanity.com/paper/2207.04969/) (2022)
- [Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification](https://mlsanity.com/paper/2308.12737/) (2023)

## Abstract (arXiv)

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.