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We developed a transparent computational large-scale imaging-based frameworkthat can distinguish between normal and metastasizing human cells. The methodrelies on fluorescence microscopy images showing the spatial organization ofactin and vimentin filaments in normal and metastasizing single cells, using acombination of multi-attention channels network and global explainabletechniques. We test a classification between normal cells (Bj primaryfibroblast), and their isogenically matched, transformed and invasivecounterpart (BjTertSV40TRasV12). Manual annotation is not trivial to automatedue to the intricacy of the biologically relevant features. In this research,we utilized established deep learning networks and our new multi-attentionchannel architecture. To increase the interpretability of the network - crucialfor this application area - we developed an interpretable global explainableapproach correlating the weighted geometric mean of the total cell images andtheir local GradCam scores. The significant results from our analysisunprecedently allowed a more detailed, and biologically relevant understandingof the cytoskeletal changes that accompany oncogenic transformation of normalto invasive and metastasizing cells. We also paved the way for a possiblespatial micrometre-level biomarker for future development of diagnostic toolsagainst metastasis (spatial distribution of vimentin).

A novel framework employing deep multi-attention channels network for  the autonomous detection of metastasizing cells through fluorescence  microscopy