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).

我们开发了一个透明的计算机大规模成像框架，可以区分正常和转移的人类细胞。该方法依赖于荧光显微镜图像，显示正常和转移的单个细胞中肌动蛋白和微丝蛋白丝的空间组织，并结合多重关注通道网络和全局可解释技术。通过深度学习网络和新的多重关注通道架构，我们对网络的可解释性进行了增强，并开发了一种可解释的全局可解释方法，将细胞图像的加权几何平均值与局部 GradCam 分数进行相关联。通过我们的分析结果，我们首次能够更详细、更具生物学意义地理解由正常细胞转变为侵袭和转移细胞伴随的细胞骨架变化。我们还为未来诊断工具开发铺平了一条可能的空间微米级生物标志物的道路（微丝蛋白的空间分布）。

一种利用深度多注意力通道网络的新型框架，用于通过荧光显微镜自动检测转移细胞

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