# Distant Domain Transfer Learning for Medical Imaging

arXiv: https://arxiv.org/abs/2012.06346 · HTML: https://mlsanity.com/paper/2012.06346/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 16 of 16)

- [Self-Supervised Pretraining Improves Self-Supervised Pretraining](https://mlsanity.com/paper/2103.12718/) (2021)
- [Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study](https://mlsanity.com/paper/2209.15517/) (2022)
- [On the Generalization for Transfer Learning: An Information-Theoretic Analysis](https://mlsanity.com/paper/2207.05377/) (2022)
- [Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2312.07965/) (2023)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition](https://mlsanity.com/paper/2306.08249/) (2023)
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- [NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks](https://mlsanity.com/paper/2303.06151/) (2023)
- ["Forgetting" in Machine Learning and Beyond: A Survey](https://mlsanity.com/paper/2405.20620/) (2024)
- [Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis](https://mlsanity.com/paper/2209.10425/) (2022)
- [Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging](https://mlsanity.com/paper/2407.06566/) (2024)
- [Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things](https://mlsanity.com/paper/2312.07437/) (2023)
- [Overcoming Negative Transfer by Online Selection: Distant Domain Adaptation for Fault Diagnosis](https://mlsanity.com/paper/2405.17493/) (2024)
- [Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging](https://mlsanity.com/paper/2407.05592/) (2024)
- [Hybrid Deep Learning-Based for Enhanced Occlusion Segmentation in PICU Patient Monitoring](https://mlsanity.com/paper/2407.13341/) (2024)
- [Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs](https://mlsanity.com/paper/2603.14271/) (2026)

## Abstract (arXiv)

Medical image processing is one of the most important topics in the field of the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical image tasks. However, conventional deep learning have two main drawbacks: 1) insufficient training data and 2) the domain mismatch between the training data and the testing data. In this paper, we propose a distant domain transfer learning (DDTL) method for medical image classification. Moreover, we apply our methods to a recent issue (Coronavirus diagnose). Several current studies indicate that lung Computed Tomography (CT) images can be used for a fast and accurate COVID-19 diagnosis. However, the well-labeled training data cannot be easily accessed due to the novelty of the disease and a number of privacy policies. Moreover, the proposed method has two components: Reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. It is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. In this study, we develop a DDTL model for COVID-19 diagnose using unlabeled Office-31, Catech-256, and chest X-ray image data sets as the source data, and a small set of COVID-19 lung CT as the target data. The main contributions of this study: 1) the proposed method benefits from unlabeled data collected from distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.