Towards scalable deep learning for automated microscopy in harmful algal bloom monitoring: Data-centric workflow and multi-region generalisation.
Negar Taheriashtiani, Glenn B. McGregor, N. Crosbie, Peter Hobson, Elloise Trotta, A. Lintern, et al.
TL;DR: A multi-region microscopy dataset comprising 105 microalgae/cyanobacteria taxa collected from three Australian regions is introduced, capturing diversity and challenges of operational monitoring, demonstrating that a structured data-centric workflow can substantially enhance automated microscopy, yet domain generalisation remains a critical bottleneck for deployment.
Harmful algal blooms (HABs) pose growing risks to drinking-water supplies and ecosystems, yet routine monitoring remains dependent on manual light microscopy. Deep learning offers a potential rapid alternative for automating microscopy, but progress has been constrained by ideali…