# CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning

arXiv: https://arxiv.org/abs/2408.04949 · HTML: https://mlsanity.com/paper/2408.04949/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 30 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [Causality-inspired Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2111.12525/) (2021)
- [Causal Attention for Interpretable and Generalizable Graph Classification](https://mlsanity.com/paper/2112.15089/) (2021)
- [Causal Attention for Unbiased Visual Recognition](https://mlsanity.com/paper/2108.08782/) (2021)
- [On the Integration of Self-Attention and Convolution](https://mlsanity.com/paper/2111.14556/) (2021)
- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [On the limits of cross-domain generalization in automated X-ray prediction](https://mlsanity.com/paper/2002.02497/) (2020)
- [Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification](https://mlsanity.com/paper/1909.01940/) (2019)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets](https://mlsanity.com/paper/2209.00613/) (2022)
- [Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning](https://mlsanity.com/paper/2111.10827/) (2021)
- [Learning to Compare Relation: Semantic Alignment for Few-Shot Learning](https://mlsanity.com/paper/2003.00210/) (2020)
- [Chest X-ray Image Classification: A Causal Perspective](https://mlsanity.com/paper/2305.12072/) (2023)
- [Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites](https://mlsanity.com/paper/2206.06813/) (2022)
- [Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI](https://mlsanity.com/paper/2309.10725/) (2023)
- [Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-rays](https://mlsanity.com/paper/2302.13991/) (2023)
- [Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results](https://mlsanity.com/paper/2309.10399/) (2023)

## Built on by (most cited first, 2 of 2)

- [FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment](https://mlsanity.com/paper/2503.23911/) (2025)
- [Locally Private Online Quantile Regression: Estimation and Inference](https://mlsanity.com/paper/2607.05312/) (2026)

## Abstract (arXiv)

Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one. For instance, a classifier trained on chest X-ray (CXR) images from one hospital may not generalize to images from another hospital due to variations in scanner settings or patient characteristics. In this paper, we introduce our CROCODILE framework, showing how tools from causality can foster a model's robustness to domain shift via feature disentanglement, contrastive learning losses, and the injection of prior knowledge. This way, the model relies less on spurious correlations, learns the mechanism bringing from images to prediction better, and outperforms baselines on out-of-distribution (OOD) data. We apply our method to multi-label lung disease classification from CXRs, utilizing over 750000 images from four datasets. Our bias-mitigation method improves domain generalization and fairness, broadening the applicability and reliability of deep learning models for a safer medical image analysis. Find our code at: https://github.com/gianlucarloni/crocodile.