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/Subject(Object detection is a fundamental task in digital microscopy, where machine learning has made great strides in overcoming the limitations of classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, experimental data are often challenging to label and cannot be easily reproduced numerically. Here, we propose a deep-learning method, named LodeSTAR \(Localization and detection from Symmetries, Translations And Rotations\), that learns to detect microscopic objects with sub-pixel accuracy from a single unlabeled experimental image by exploiting the inherent roto-translational symmetries of this task. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy, also when analyzing challenging experimental data containing densely packed cells or noisy backgrounds. Furthermore, by exploiting additional symmetries we show that LodeSTAR can measure other properties, e.g., vertical position and polarizability in holographic microscopy.)
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/Title(Single-shot self-supervised object detection in microscopy)
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doi:10.1038/s41467-022-35004-y
Single-shot self-supervised object detection in microscopy
Benjamin Midtvedt
Jesús Pineda
Fredrik Skärberg
Erik Olsén
Harshith Bachimanchi
Emelie Wesén
Elin K. Esbjörner
Erik Selander
Fredrik Höök
Daniel Midtvedt
Giovanni Volpe
Springer US
Nature Communications, doi:10.1038/s41467-022-35004-y
Springer
2022-12-05T17:31:32+01:00
2022-12-02T15:39:22+05:30
2022-12-05T17:31:32+01:00
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