Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
Pingyi Chen Chenglu Zhu Zhongyi Shui Jiatong Cai Sunyi Zheng Shichuan Zhang Lin Yang
The success of supervised deep learning models on cell recognition tasks
relies on detailed annotations. Many previous works have managed to reduce the
dependency on labels. However, considering the large number of cells contained
in a patch, costly and inefficient labeling is still inevitable. To this end,
we explored label-free methods for cell recognition. Prior self-activation maps
(PSM) are proposed to generate pseudo masks as training targets. To be
specific, an activation network is trained with self-supervised learning. The
gradient information in the shallow layers of the network is aggregated to
generate prior self-activation maps. Afterward, a semantic clustering module is
then introduced as a pipeline to transform PSMs to pixel-level semantic pseudo
masks for downstream tasks. We evaluated our method on two histological
datasets: MoNuSeg (cell segmentation) and BCData (multi-class cell detection).
Compared with other fully-supervised and weakly-supervised methods, our method
can achieve competitive performance without any manual annotations. Our simple
but effective framework can also achieve multi-class cell detection which can
not be done by existing unsupervised methods. The results show the potential of
PSMs that might inspire other research to deal with the hunger for labels in
medical area.