# MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classification

arXiv: https://arxiv.org/abs/2311.03052 · HTML: https://mlsanity.com/paper/2311.03052/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 23 of 23)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks](https://mlsanity.com/paper/1905.11001/) (2019)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images](https://mlsanity.com/paper/2303.06873/) (2023)
- [TransMix: Attend to Mix for Vision Transformers](https://mlsanity.com/paper/2111.09833/) (2021)
- [Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2103.10626/) (2021)
- [SuperMix: Supervising the Mixing Data Augmentation](https://mlsanity.com/paper/2003.05034/) (2020)
- [Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential](https://mlsanity.com/paper/2206.04425/) (2022)
- [Weakly supervised multiple instance learning histopathological tumor segmentation](https://mlsanity.com/paper/2004.05024/) (2020)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Structured State Space Models for Multiple Instance Learning in Digital Pathology](https://mlsanity.com/paper/2306.15789/) (2023)
- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning](https://mlsanity.com/paper/2210.09452/) (2022)
- [A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning](https://mlsanity.com/paper/2102.03837/) (2021)
- [MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis](https://mlsanity.com/paper/2211.05862/) (2022)
- [Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification](https://mlsanity.com/paper/2303.15749/) (2023)
- [Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks](https://mlsanity.com/paper/2012.08158/) (2020)
- [Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data](https://mlsanity.com/paper/2203.15804/) (2022)
- [Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection](https://mlsanity.com/paper/2307.09457/) (2023)

## Built on by (most cited first, 1 of 1)

- [Controllable Latent Space Augmentation for Digital Pathology](https://mlsanity.com/paper/2508.14588/) (2025)

## Abstract (arXiv)

For classifying digital whole slide images in the absence of pixel level annotation, typically multiple instance learning methods are applied. Due to the generic applicability, such methods are currently of very high interest in the research community, however, the issue of data augmentation in this context is rarely explored. Here we investigate linear and multilinear interpolation between feature vectors, a data augmentation technique, which proved to be capable of improving the generalization performance classification networks and also for multiple instance learning. Experiments, however, have been performed on only two rather small data sets and one specific feature extraction approach so far and a strong dependence on the data set has been identified. Here we conduct a large study incorporating 10 different data set configurations, two different feature extraction approaches (supervised and self-supervised), stain normalization and two multiple instance learning architectures. The results showed an extraordinarily high variability in the effect of the method. We identified several interesting aspects to bring light into the darkness and identified novel promising fields of research.