Gaurav Fotedar

Weinan Song, Gaurav Fotedar, Nima Tajbakhsh et al.

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.

Gaurav Fotedar, Nima Tajbakhsh, Shilpa Ananth et al.

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.