# Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2405.03235 · HTML: https://mlsanity.com/paper/2405.03235/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020)
- [Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review](https://mlsanity.com/paper/2102.01530/) (2021)
- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [Comprehensive Survey of Model Compression and Speed up for Vision Transformers](https://mlsanity.com/paper/2404.10407/) (2024)
- [Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis](https://mlsanity.com/paper/1904.08138/) (2019)
- [Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection](https://mlsanity.com/paper/2404.10026/) (2024)
- [Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography](https://mlsanity.com/paper/1908.09993/) (2019)
- [Learning Robust Heterogeneous Signal Features from Parallel Neural Network for Audio Sentiment Analysis](https://mlsanity.com/paper/1811.08065/) (2018)
- [Machine learning-based system reliability analysis with Gaussian Process Regression](https://mlsanity.com/paper/2403.11125/) (2024)
- [Navigating the Landscape of Distributed File Systems: Architectures, Implementations, and Considerations](https://mlsanity.com/paper/2403.15701/) (2024)

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

- [MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning](https://mlsanity.com/paper/2602.20223/) (2026)
- [Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI](https://mlsanity.com/paper/2605.03490/) (2026)

## Abstract (arXiv)

Brain disorders are a major challenge to global health, causing millions of deaths each year. Accurate diagnosis of these diseases relies heavily on advanced medical imaging techniques such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, the scarcity of annotated data poses a significant challenge in deploying machine learning models for medical diagnosis. To address this limitation, deep learning techniques have shown considerable promise. Domain adaptation techniques enhance a model's ability to generalize across imaging modalities by transferring knowledge from one domain (e.g., CT images) to another (e.g., MRI images). Such cross-modality adaptation is essential to improve the ability of models to consistently generalize across different imaging modalities. This study collected relevant resources from the Kaggle website and employed the Maximum Mean Difference (MMD) method - a popular domain adaptation method - to reduce the differences between imaging domains. By combining MMD with Convolutional Neural Networks (CNNs), the accuracy and utility of the model is obviously enhanced. The excellent experimental results highlight the great potential of data-driven domain adaptation techniques to improve diagnostic accuracy and efficiency, especially in resource-limited environments. By bridging the gap between different imaging modalities, the study aims to provide clinicians with more reliable diagnostic tools.