# Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels

arXiv: https://arxiv.org/abs/2102.08148 · HTML: https://mlsanity.com/paper/2102.08148/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [MixUp as Locally Linear Out-Of-Manifold Regularization](https://mlsanity.com/paper/1809.02499/) (2018)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks](https://mlsanity.com/paper/1803.04565/) (2018)
- [Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment](https://mlsanity.com/paper/1906.07775/) (2019)
- [InfoMask: Masked Variational Latent Representation to Localize Chest Disease](https://mlsanity.com/paper/1903.11741/) (2019)
- [GraphXNET- Chest X-Ray Classification Under Extreme Minimal Supervision](https://mlsanity.com/paper/1907.10085/) (2019)
- [Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation](https://mlsanity.com/paper/1910.04961/) (2019)
- [Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays](https://mlsanity.com/paper/1908.10468/) (2019)
- [Deep Multi Label Classification in Affine Subspaces](https://mlsanity.com/paper/1907.04563/) (2019)

## Built on by (most cited first, 4 of 4)

- [ExcelFormer: A neural network surpassing GBDTs on tabular data](https://mlsanity.com/paper/2301.02819/) (2023)
- [Personalized Heart Disease Detection via ECG Digital Twin Generation](https://mlsanity.com/paper/2404.11171/) (2024)
- [Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds](https://mlsanity.com/paper/2403.09598/) (2024)
- [Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network](https://mlsanity.com/paper/2206.10592/) (2022)

## Abstract (arXiv)

In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expert-level performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, called Flow-Mixup, for multi-labeled medical image classification with corrupted labels. Flow-Mixup guides the models to capture robust features for each abnormality, thus helping handle corrupted labels effectively and making it possible to apply automatic annotation. Specifically, Flow-Mixup decouples the extracted features by adding constraints to the hidden states of the models. Also, Flow-Mixup is more stable and effective comparing to other known regularization methods, as shown by theoretical and empirical analyses. Experiments on two electrocardiogram datasets and a chest X-ray dataset containing corrupted labels verify that Flow-Mixup is effective and insensitive to corrupted labels.