# Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

arXiv: https://arxiv.org/abs/2110.06803 · HTML: https://mlsanity.com/paper/2110.06803/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 52 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Simultaneous Deep Transfer Across Domains and Tasks](https://mlsanity.com/paper/1510.02192/) (2015)
- [Generate To Adapt: Aligning Domains using Generative Adversarial Networks](https://mlsanity.com/paper/1704.01705/) (2017)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes](https://mlsanity.com/paper/1707.09465/) (2017)
- [Unsupervised domain adaptation for medical imaging segmentation with self-ensembling](https://mlsanity.com/paper/1811.06042/) (2018)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [Disentangled Representation Learning in Cardiac Image Analysis](https://mlsanity.com/paper/1903.09467/) (2019)
- [The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study](https://mlsanity.com/paper/1911.00515/) (2019)
- [A Learning Strategy for Contrast-agnostic MRI Segmentation](https://mlsanity.com/paper/2003.01995/) (2020)
- [Joint Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2006.10297/) (2020)
- [One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks](https://mlsanity.com/paper/1805.12415/) (2018)
- [Residual Parameter Transfer for Deep Domain Adaptation](https://mlsanity.com/paper/1711.07714/) (2017)
- [Adversarial Transfer Learning for Cross-domain Visual Recognition](https://mlsanity.com/paper/1711.08904/) (2017)
- [Projective Latent Interventions for Understanding and Fine-tuning Classifiers](https://mlsanity.com/paper/2006.12902/) (2020)
- [Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation](https://mlsanity.com/paper/2109.09736/) (2021)

## Built on by (most cited first, 5 of 5)

- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2307.04513/) (2023)
- [Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images](https://mlsanity.com/paper/2303.04911/) (2023)
- [Learning from Heterogeneous Structural MRI via Collaborative Domain Adaptation for Late-Life Depression Assessment](https://mlsanity.com/paper/2507.22321/) (2025)
- [Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation](https://mlsanity.com/paper/2603.13328/) (2026)

## Abstract (arXiv)

The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine. This is especially the case for Magnetic Resonance (MR) images, where different MR scanners introduce a bias that limits the performance of a machine learning model. We present a novel method that learns to ignore the scanner-related features present in MR images, by introducing specific additional constraints on the latent space. We focus on a real-world classification scenario, where only a small dataset provides images of all classes. Our method textitLearn to Ignore (L2I) outperforms state-of-the-art domain adaptation methods on a multi-site MR dataset for a classification task between multiple sclerosis patients and healthy controls.