# Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images

arXiv: https://arxiv.org/abs/2303.08632 · HTML: https://mlsanity.com/paper/2303.08632/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [Restricting the Flow: Information Bottlenecks for Attribution](https://mlsanity.com/paper/2001.00396/) (2020)
- [When Explanations Lie: Why Many Modified BP Attributions Fail](https://mlsanity.com/paper/1912.09818/) (2019)
- [A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations](https://mlsanity.com/paper/1805.07039/) (2018)
- [Neural Response Interpretation through the Lens of Critical Pathways](https://mlsanity.com/paper/2103.16886/) (2021)
- [Do Explanations Explain? Model Knows Best](https://mlsanity.com/paper/2203.02269/) (2022)
- [Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information](https://mlsanity.com/paper/2110.01471/) (2021)
- [Attention based Multiple Instance Learning for Classification of Blood Cell Disorders](https://mlsanity.com/paper/2007.11641/) (2020)
- [Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features](https://mlsanity.com/paper/2104.00411/) (2021)
- [Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models](https://mlsanity.com/paper/2104.02481/) (2021)
- [Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods](https://mlsanity.com/paper/2012.00362/) (2020)
- [Interpretable Vertebral Fracture Diagnosis](https://mlsanity.com/paper/2203.16273/) (2022)

## Built on by (most cited first, 9 of 9)

- [SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology](https://mlsanity.com/paper/2312.15010/) (2023)
- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images](https://mlsanity.com/paper/2404.05584/) (2024)
- [Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology](https://mlsanity.com/paper/2603.08328/) (2026)
- [HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis](https://mlsanity.com/paper/2408.08847/) (2024)
- [Novel Pipeline for Diagnosing Acute Lymphoblastic Leukemia Sensitive to Related Biomarkers](https://mlsanity.com/paper/2307.04014/) (2023)
- [A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes](https://mlsanity.com/paper/2308.12675/) (2023)
- [Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder](https://mlsanity.com/paper/2408.06720/) (2024)
- [Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations](https://mlsanity.com/paper/2608.28820/) (2026)

## Abstract (arXiv)

Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we investigate the use of four attribution methods to explain a multiple instance learning models: GradCAM, Layer-Wise Relevance Propagation (LRP), Information Bottleneck Attribution (IBA), and InputIBA. With this collection of methods, we can derive pixel-level explanations on for the task of diagnosing blood cancer from patients' blood smears. We study two datasets of acute myeloid leukemia with over 100 000 single cell images and observe how each attribution method performs on the multiple instance learning architecture focusing on different properties of the white blood single cells. Additionally, we compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard. Our study addresses the challenge of implementing pixel-level explainability in multiple instance learning models and provides insights for clinicians to better understand and trust decisions from computer-aided diagnosis systems.