Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy
Fig 7
Performance evaluation of Cell-DINO on the CPG datasets.
A) The evaluation task is mechanism of action (MoA) prediction, where chemical compounds are used to treat cells and Cell Painting images are collected to assess their effect and infer their MoA with computational models. B) Illustration of the steps of the computational workflow to predict MoA from Cell Painting images. The only step that changes in these experiments is feature extraction (highlighted in bold). C) Box plots comparing the distributions of performance for the evaluated feature extraction methods. Horizontal axis: evaluated methods organized by feature type. Vertical axis: performance score according to the area under the precision-recall curve of the multi-class MoA prediction problem [62]. Highlighted numbers in the boxes are the median values of the distribution.