# Impact of domain adaptation in deep learning for medical image classifications

arXiv: https://arxiv.org/abs/2602.09355 · HTML: https://mlsanity.com/paper/2602.09355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Cross-Domain Adaptive Teacher for Object Detection](https://mlsanity.com/paper/2111.13216/) (2021)
- [A Consistent and Efficient Evaluation Strategy for Attribution Methods](https://mlsanity.com/paper/2202.00449/) (2022)
- [TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2108.05988/) (2021)
- [Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift](https://mlsanity.com/paper/2207.00769/) (2022)
- [Towards Accurate and Robust Domain Adaptation under Noisy Environments](https://mlsanity.com/paper/2004.12529/) (2020)
- [Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification](https://mlsanity.com/paper/2502.10694/) (2025)
- [Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview](https://mlsanity.com/paper/2503.08420/) (2025)
- [FAA-CLIP: Federated Adversarial Adaptation of CLIP](https://mlsanity.com/paper/2503.05776/) (2025)

## Abstract (arXiv)

Domain adaptation (DA) is a quickly expanding area in machine learning that involves adjusting a model trained in one domain to perform well in another domain. While there have been notable progressions, the fundamental concept of numerous DA methodologies has persisted: aligning the data from various domains into a shared feature space. In this space, knowledge acquired from labeled source data can improve the model training on target data that lacks sufficient labels. In this study, we demonstrate the use of 10 deep learning models to simulate common DA techniques and explore their application in four medical image datasets. We have considered various situations such as multi-modality, noisy data, federated learning (FL), interpretability analysis, and classifier calibration. The experimental results indicate that using DA with ResNet34 in a brain tumor (BT) data set results in an enhancement of 4.7% in model performance. Similarly, the use of DA can reduce the impact of Gaussian noise, as it provides ~ 3% accuracy increase using ResNet34 on a BT dataset. Furthermore, simply introducing DA into FL framework shows limited potential (e.g., ~ 0.3% increase in performance) for skin cancer classification. In addition, the DA method can improve the interpretability of the models using the gradcam++ technique, which offers clinical values. Calibration analysis also demonstrates that using DA provides a lower expected calibration error (ECE) value ~ 2% compared to CNN alone on a multi-modality dataset.