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

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, most domain transfer methods mainly rely on adversarial networks and weak cycle consistency, which could result in incomplete domain transfer or poor adherence to the original image content. In this paper, we introduce MDT-Net to address the limitations above through a multi-domain transfer model based on perceptual supervision. Specifically, our model consists of an encoder-decoder network, which aims to preserve anatomical structures, and multiple domain-specific transfer modules, which guide the domain transition through feature transformation. During the inference, MDT-Net can directly transfer images from the source domain to multiple target domains at one time without any reference image. To demonstrate the performance of MDT-Net, we evaluate it on RETOUCH dataset, comprising OCT scans from three different scanner devices (domains), for multi-domain transfer. We also take the transformed results as additional training images for fluid segmentation in OCT scans in the tasks of domain adaptation and data augmentation. Experimental results show that MDT-Net can outperform other domain transfer models qualitatively and quantitatively. Furthermore, the significant improvement in dice scores over multiple segmentation models also demonstrates the effectiveness and efficiency of our proposed method.

Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts

Supervised learning has proved effective for medical image analysis. However, it can utilize only the small labeled portion of data; it fails to leverage the large amounts of unlabeled data that is often available in medical image datasets. Supervised models are further handicapped by domain shifts, when the labeled dataset, despite being large enough, fails to cover different protocols or ethnicities. In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student semi-supervised paradigm. Extreme consistency is the process of sending an extreme transformation of a given image to the student network and then constraining its prediction to be consistent with the teacher network's prediction for the untransformed image. The extreme nature of our consistency loss distinguishes our method from related works that yield suboptimal performance by exercising only mild prediction consistency. Our method is 1) auto-didactic, as it requires no extra expert annotations; 2) versatile, as it handles both domain shift and limited annotation problems; 3) generic, as it is readily applicable to classification, segmentation, and detection tasks; and 4) simple to implement, as it requires no adversarial training. We evaluate our method for the tasks of lesion and retinal vessel segmentation in skin and fundus images. Our experiments demonstrate a significant performance gain over both modern supervised networks and recent semi-supervised models. This performance is attributed to the strong regularization enforced by extreme consistency, which enables the student network to learn how to handle extreme variants of both labeled and unlabeled images. This enhances the network's ability to tackle the inevitable same- and cross-domain data variability during inference.