2022/08/05 by Marawan Elbatel, Christina Bornberg, Elbatel, Marawan +10
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Algorithm #Annotation #Artificial intelligence #Artificial neural network #Bounding overwatch #Cell Image Analysis Techniques #Code (set theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #Error detection and correction #FOS: Computer and information sciences #FOS: Electrical engineering #Image (mathematics) #Image Processing Techniques and Applications #Image and Video Processing (eess.IV) #Imperfect #Industrial Vision Systems and Defect Detection #Machine learning #Minimum bounding box #Object (grammar) #Object detection #Pattern recognition (psychology) #Segmentation #Set (abstract data type) #Supervised learning #Synthetic data #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2208.03327
published in arXiv (Cornell University) (Cornell University) · To appear at MICCAI 2022 Workshop on Domain Adaptation and Representation Transfer (DART)
arxiv created 2022/08/05 · openalex publication_date 2022/08/05 · arxiv updated 2022/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.