# Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

arXiv: https://arxiv.org/abs/1908.06943 · HTML: https://mlsanity.com/paper/1908.06943/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 30 of 32)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
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- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology](https://mlsanity.com/paper/2204.05044/) (2022)
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- [Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI](https://mlsanity.com/paper/2205.01929/) (2022)
- [Attention-based Interpretable Regression of Gene Expression in Histology](https://mlsanity.com/paper/2208.13776/) (2022)
- [Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology](https://mlsanity.com/paper/2303.07771/) (2023)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Multi-Attention Multiple Instance Learning](https://mlsanity.com/paper/2112.06071/) (2021)
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- [Automatic Infectious Disease Classification Analysis with Concept Discovery](https://mlsanity.com/paper/2209.02415/) (2022)
- [Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology](https://mlsanity.com/paper/2603.08328/) (2026)
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- [Preparing data for pathological artificial intelligence with clinical-grade performance](https://mlsanity.com/paper/2205.10748/) (2022)
- [From slides (through tiles) to pixels: an explainability framework for weakly supervised models in pre-clinical pathology](https://mlsanity.com/paper/2302.01653/) (2023)
- [Multiple Instance Learning with Trainable Decision Tree Ensembles](https://mlsanity.com/paper/2302.06601/) (2023)
- [Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification](https://mlsanity.com/paper/2308.12737/) (2023)
- [Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging](https://mlsanity.com/paper/2407.00538/) (2024)
- [The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review](https://mlsanity.com/paper/2409.07347/) (2024)
- [Explaining Digital Pathology Models via Clustering Activations](https://mlsanity.com/paper/2511.14558/) (2025)

## Abstract (arXiv)

Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, explanation methods have emerged, which are so far still rarely used in medicine. This work shows their application to generate heatmaps that allow to resolve common challenges encountered in deep learning-based digital histopathology analyses. These challenges comprise biases typically inherent to histopathology data. We study binary classification tasks of tumor tissue discrimination in publicly available haematoxylin and eosin slides of various tumor entities and investigate three types of biases: (1) biases which affect the entire dataset, (2) biases which are by chance correlated with class labels and (3) sampling biases. While standard analyses focus on patch-level evaluation, we advocate pixel-wise heatmaps, which offer a more precise and versatile diagnostic instrument and furthermore help to reveal biases in the data. This insight is shown to not only detect but also to be helpful to remove the effects of common hidden biases, which improves generalization within and across datasets. For example, we could see a trend of improved area under the receiver operating characteristic curve by 5% when reducing a labeling bias. Explanation techniques are thus demonstrated to be a helpful and highly relevant tool for the development and the deployment phases within the life cycle of real-world applications in digital pathology.