A novel framework employing deep multi-attention channels network for
  the autonomous detection of metastasizing cells through fluorescence
  microscopy
Michail Mamalakis Sarah C. Macfarlane Scott V. Notley Annica K. B Gad George Panoutsos
We developed a transparent computational large-scale imaging-based framework
that can distinguish between normal and metastasizing human cells. The method
relies on fluorescence microscopy images showing the spatial organization of
actin and vimentin filaments in normal and metastasizing single cells, using a
combination of multi-attention channels network and global explainable
techniques. We test a classification between normal cells (Bj primary
fibroblast), and their isogenically matched, transformed and invasive
counterpart (BjTertSV40TRasV12). Manual annotation is not trivial to automate
due to the intricacy of the biologically relevant features. In this research,
we utilized established deep learning networks and our new multi-attention
channel architecture. To increase the interpretability of the network - crucial
for this application area - we developed an interpretable global explainable
approach correlating the weighted geometric mean of the total cell images and
their local GradCam scores. The significant results from our analysis
unprecedently allowed a more detailed, and biologically relevant understanding
of the cytoskeletal changes that accompany oncogenic transformation of normal
to invasive and metastasizing cells. We also paved the way for a possible
spatial micrometre-level biomarker for future development of diagnostic tools
against metastasis (spatial distribution of vimentin).