  In regenerative medicine research, we experimentally design the composition
of chemical medium. We add different components to 384-well plates and culture
the biological cells. We monitor the condition of the cells and take time-lapse
bioimages for morphological assay. In particular, precipitation can appear as
artefacts in the image and contaminate the noise in the imaging assay.
Inspecting precipitates is a tedious task for the observer, and differences in
experience can lead to variations in judgement from person to person. The
machine learning approach will remove the burden of human inspection and
provide consistent inspection. In addition, precipitation features are as small
as 10-20 {\mu}m. A 1200 pixel square well image resized under a resolution of
2.82 {\mu}m/pixel will result in a reduction in precipitation features.
Dividing the well images into 240-pixel squares and learning without resizing
preserves the resolution of the original image. In this study, we developed an
application to automatically detect precipitation on 384-well plates utilising
optical microscope images. We apply MN-pair contrastive clustering to extract
precipitation classes from approximately 20,000 patch images. To detect
precipitation features, we compare deeper FCDDs detectors with optional
backbones and build a machine learning pipeline to detect precipitation from
the maximum score of quadruplet well images using isolation Forest algorithm,
where the anomaly score is ranged from zero to one. Furthermore, using this
application we can visualise precipitation situ heatmap on a 384-well plate.
