Pseudo-Labeling Enhanced by Privileged Information and Its Application
  to In Situ Sequencing Images
Marzieh Haghighi Mario C. Cruz Erin Weisbart Beth A. Cimini Avtar Singh Julia Bauman Maria E. Lozada Sanam L. Kavari James T. Neal Paul C. Blainey Anne E. Carpenter Shantanu Singh
Various strategies for label-scarce object detection have been explored by
the computer vision research community. These strategies mainly rely on
assumptions that are specific to natural images and not directly applicable to
the biological and biomedical vision domains. For example, most semi-supervised
learning strategies rely on a small set of labeled data as a confident source
of ground truth. In many biological vision applications, however, the ground
truth is unknown and indirect information might be available in the form of
noisy estimations or orthogonal evidence. In this work, we frame a crucial
problem in spatial transcriptomics - decoding barcodes from In-Situ-Sequencing
(ISS) images - as a semi-supervised object detection (SSOD) problem. Our
proposed framework incorporates additional available sources of information
into a semi-supervised learning framework in the form of privileged
information. The privileged information is incorporated into the teacher's
pseudo-labeling in a teacher-student self-training iteration. Although the
available privileged information could be data domain specific, we have
introduced a general strategy of pseudo-labeling enhanced by privileged
information (PLePI) and exemplified the concept using ISS images, as well on
the COCO benchmark using extra evidence provided by CLIP.