Transformer-Based Detection of Microorganisms on High-Resolution Petri Dish Images

Nikolas Ebert, Didier Stricker, Oliver Wasenm├╝ller; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 3961-3970

Abstract


Many medical or pharmaceutical processes have strict guidelines regarding continuous hygiene monitoring. This often involves the labor-intensive task of manually counting microorganisms in Petri dishes by trained personnel. Automation attempts often struggle due to major challenges: significant scaling differences, low separation, low contrast, etc. To address these challenges, we introduce AttnPAFPN, a high-resolution detection pipeline that leverages a novel transformer variation, the efficient-global self-attention mechanism. Our streamlined approach can be easily integrated in almost any multi-scale object detection pipeline. In a comprehensive evaluation on the publicly available AGAR dataset, we demonstrate the superior accuracy of our network over the current state-of-the-art. In order to demonstrate the task-independent performance of our approach, we perform further experiments on COCO and LIVECell datasets.

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[bibtex]
@InProceedings{Ebert_2023_ICCV, author = {Ebert, Nikolas and Stricker, Didier and Wasenm\"uller, Oliver}, title = {Transformer-Based Detection of Microorganisms on High-Resolution Petri Dish Images}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2023}, pages = {3961-3970} }