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; Daniel Midtvedt; Giovanni Volpe

doi:10.1117/12.2678329

28 September 2023 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, Daniel Midtvedt, Giovanni Volpe

Author Affiliations +

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550P (2023) https://doi.org/10.1117/12.2678329
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

We present LodeSTAR, an unsupervised, single-shot object detector for microscopy. LodeSTAR exploits the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. We demonstrate that LodeSTAR is comparable to state-of-the-art, supervised deep learning methods, despite training on orders of magnitude less training data, and no annotations. Moreover, we demonstrate that LodeSTAR achieves near theoretically optimal results in terms of sub-pixel positioning of objects of various shapes. Finally, we show that LodeSTAR can exploit additional symmetries to measure additional particle properties, such as the axial position of objects and particle polarizability.

Conference Presentation

Citation Download Citation

Benjamin Midtvedt, Jesús Pineda, Fredrik Skärberg, Erik Olsén, Harshith Bachimanchi, Emelie Wesén, Elin K. Esbjörner, Erik Selander, Daniel Midtvedt, and Giovanni Volpe "Single-shot self-supervised object detection in microscopy", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550P (28 September 2023); https://doi.org/10.1117/12.2678329

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