  A common shortfall of supervised learning for medical imaging is the greedy
need for human annotations, which is often expensive and time-consuming to
obtain. This paper proposes a semi-supervised classification method for three
kinds of apicomplexan parasites and non-infected host cells microscopic images,
which uses a small number of labeled data and a large number of unlabeled data
for training. There are two challenges in microscopic image recognition. The
first is that salient structures of the microscopic images are more fuzzy and
intricate than natural images' on a real-world scale. The second is that
insignificant textures, like background staining, lightness, and contrast
level, vary a lot in samples from different clinical scenarios. To address
these challenges, we aim to learn a distinguishable and appearance-invariant
representation by contrastive learning strategy. On one hand, macroscopic
images, which share similar shape characteristics in morphology, are introduced
to contrast for structure enhancement. On the other hand, different appearance
transformations, including color distortion and flittering, are utilized to
contrast for texture elimination. In the case where only 1% of microscopic
images are labeled, the proposed method reaches an accuracy of 94.90% in a
generalized testing set.
