  Lesion detection serves a critical role in early diagnosis and has been well
explored in recent years due to methodological advancesand increased data
availability. However, the high costs of annotations hinder the collection of
large and completely labeled datasets, motivating semi-supervised detection
approaches. In this paper, we introduce mean teacher hetero-modal detection
(MTHD), which addresses two important gaps in current semi-supervised
detection. First, it is not obvious how to enforce unlabeled consistency
constraints across the very different outputs of various detectors, which has
resulted in various compromises being used in the state of the art. Using an
anchor-free framework, MTHD formulates a mean teacher approach without such
compromises, enforcing consistency on the soft-output of object centers and
size. Second, multi-sequence data is often critical, e.g., for abdominal lesion
detection, but unlabeled data is often missing sequences. To deal with this,
MTHD incorporates hetero-modal learning in its framework. Unlike prior art,
MTHD is able to incorporate an expansive set of consistency constraints that
include geometric transforms and random sequence combinations. We train and
evaluate MTHD on liver lesion detection using the largest MR lesion dataset to
date (1099 patients with >5000 volumes). MTHD surpasses the best
fully-supervised and semi-supervised competitors by 10.1% and 3.5%,
respectively, in average sensitivity.
