  In-vitro tests are an alternative to animal testing for the toxicity of
medical devices. Detecting cells as a first step, a cell expert evaluates the
growth of cells according to cytotoxicity grade under the microscope. Thus,
human fatigue plays a role in error making, making the use of deep learning
appealing. Due to the high cost of training data annotation, an approach
without manual annotation is needed. We propose Seamless Iterative
Semi-Supervised correction of Imperfect labels (SISSI), a new method for
training object detection models with noisy and missing annotations in a
semi-supervised fashion. Our network learns from noisy labels generated with
simple image processing algorithms, which are iteratively corrected during
self-training. Due to the nature of missing bounding boxes in the pseudo
labels, which would negatively affect the training, we propose to train on
dynamically generated synthetic-like images using seamless cloning. Our method
successfully provides an adaptive early learning correction technique for
object detection. The combination of early learning correction that has been
applied in classification and semantic segmentation before and synthetic-like
image generation proves to be more effective than the usual semi-supervised
approach by > 15% AP and > 20% AR across three different readers. Our code is
available at https://github.com/marwankefah/SISSI.
