Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training (1711.06606v2)

Abstract: To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire because labeled medical images are not usually available due to privacy issues, lack of experts available for annotation, underrepresentation of rare conditions and poor standardization. Lack of annotated data has been addressed in conventional vision applications using synthetic images refined via unsupervised adversarial training to look like real images. However, this approach is difficult to extend to general medical imaging because of the complex and diverse set of features found in real human tissues. We propose an alternative framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and hypothesize that clinically-relevant features can be preserved via self-regularization. These domain-adapted images can then be accurately interpreted by networks trained on large datasets of synthetic medical images. We test this approach for the notoriously difficult task of depth-estimation from endoscopy. We train a depth estimator on a large dataset of synthetic images generated using an accurate forward model of an endoscope and an anatomically-realistic colon. This network predicts significantly better depths when using synthetic-like domain-adapted images compared to the real images, confirming that the clinically-relevant features of depth are preserved.

• The paper introduces a reverse domain adaptation framework that transforms real medical images into synthetic-like representations using adversarial training.
• It leverages a large synthetic dataset and a transformer-discriminator model to significantly improve depth estimation, achieving NRMSE of 0.23 and 0.32.
• The approach retains clinically relevant features through self-regularization, offering potential for robust, automated medical image analysis.

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training

The paper presents a novel approach to address one of the pivotal challenges in medical imaging—insufficient annotated datasets for deep learning model training. Traditional methods leverage synthetic images for domain adaptation by making them appear more realistic; however, this paper investigates the reverse process, transforming real medical images into synthetic-like representations through unsupervised adversarial training. This innovative framework is particularly tailored to overcome the complexities inherent in medical images, which are often characterized by a diverse array of anatomical features and patient-specific details.

Core Contributions

The authors introduce a reverse domain adaptation methodology that transforms real images into synthetic-like representations using adversarial training. Key contributions are as follows:

1. Reverse Flow Framework: By employing a reverse flow, the framework successfully bridges the domain gap by making real medical images visually resemble synthetic images. This approach ensures that clinically-relevant features are retained through self-regularization mechanisms, making it possible for pre-trained networks on synthetic datasets to effectively interpret real images.
2. Synthetic Data Generation: A substantial and accurately labeled synthetic dataset was created, using a realistic forward model of an endoscope and a detailed anatomical colon model. The dataset comprises 260,000 images with ground truth depth, enabling effective endoscopy depth estimation training.
3. Transformer and Discriminator Model: A novel adversarial network setup transforms real-world medical images using transformer networks. It includes a discriminator trained to discern transformed images from synthetic ones, enhancing domain consistency.
4. Comprehensive Validation: The reverse adaptation approach is validated on different datasets, including images captured from a colon phantom and a real porcine colon. The methodology significantly improved depth prediction accuracy when compared to baseline results without domain adaptation.

Results and Implications

The quantitative results demonstrate a marked improvement in depth estimation with transformed images, achieving a normalized root mean square error (NRMSE) of 0.23 for colon phantoms and 0.32 for real porcine colon data, showcasing the framework's effectiveness. The technique also significantly surpassed contemporary methods, showing a better structural similarity index (SSIM) than conventional dictionary learning techniques.

The paper's primary implication suggests that transforming real medical images to synthetic-like forms offers a viable pathway to leverage synthetic training datasets. This approach presents potential applications across various clinical contexts, providing an automated approach to medical image analysis that accounts for the scarce availability of annotated medical data.

Future Directions

While the approach establishes a robust framework for enhancing medical image interpretation through domain adaptation, further research can expand this model into other medical imaging modalities. Potential future developments may include refining the balance between retaining clinically relevant features while discarding redundant patient-specific details, possibly integrating more sophisticated self-regularization terms and exploring adaptive learning rate techniques.

In conclusion, this paper presents a substantial step forward in computational medical image processing, offering a structured methodology that paves the way for robust, standardized, and clinically viable deep learning applications in medical diagnostics.