Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images

• URL: http://arxiv.org/abs/2208.03327v1
• Date: Fri, 5 Aug 2022 18:52:20 GMT
• Title: Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images
• Authors: Marawan Elbatel, Christina Bornberg, Manasi Kattel, Enrique Almar, Claudio Marrocco, Alessandro Bria
• Abstract summary: In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Human fatigue plays a role in error making, making the use of deep learning appealing. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI) Our method successfully provides an adaptive early learning correction technique for object detection.
• Score: 57.42492501915773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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