  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.
