# MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan

arXiv: https://arxiv.org/abs/2203.06363 · HTML: https://mlsanity.com/paper/2203.06363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [StyleBank: An Explicit Representation for Neural Image Style Transfer](https://mlsanity.com/paper/1703.09210/) (2017)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal](https://mlsanity.com/paper/1808.10848/) (2018)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization](https://mlsanity.com/paper/2102.06315/) (2021)
- [Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data](https://mlsanity.com/paper/1909.09716/) (2019)
- [Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI](https://mlsanity.com/paper/1910.10035/) (2019)
- [Style transfer-based image synthesis as an efficient regularization technique in deep learning](https://mlsanity.com/paper/1905.10974/) (2019)
- [StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification](https://mlsanity.com/paper/2007.05008/) (2020)

## Abstract (arXiv)

Deep learning models tend to underperform in the presence of domain shifts. Domain transfer has recently emerged as a promising approach wherein images exhibiting a domain shift are transformed into other domains for augmentation or adaptation. However, with the absence of paired and annotated images, models merely learned by adversarial loss and cycle consistency loss could result in poor consistency of anatomy structures during the translation. Additionally, the complexity of learning multi-domain transfer could significantly increase with the number of target domains and source images. In this paper, we propose a multi-domain transfer network, named MDT-Net, to address the limitations above through perceptual supervision. Specifically, our model consists of a single encoder-decoder network and multiple domain-specific transfer modules to disentangle feature representations of the anatomy content and domain variance. Owing to this architecture, the model could significantly reduce the complexity when the translation is conducted among multiple domains. To demonstrate the performance of our method, we evaluate our model qualitatively and quantitatively on RETOUCH, an OCT dataset comprising scans from three different scanner devices (domains). Furthermore, we take the transfer results as additional training data for fluid segmentation to prove the advantage of our model indirectly, i.e., in the task of data adaptation and augmentation. Experimental results show that our method could bring universal improvement in these segmentation tasks, which demonstrates the effectiveness and efficiency of MDT-Net in multi-domain transfer.