  Digital gigapixel whole slide image (WSI) is widely used in clinical
diagnosis, and automated WSI analysis is key for computer-aided diagnosis.
Currently, analyzing the integrated descriptor of probabilities or feature maps
from massive local patches encoded by ResNet classifier is the main manner for
WSI-level prediction. Feature representations of the sparse and tiny lesion
cells in cervical slides, however, are still challenging, while the unused
location representations are available to supply the semantics classification.
This study designs a novel and efficient framework with a new module InCNet
constructed lightweight model YOLCO (You Only Look Cytology Once). It directly
extracts feature inside the single cell (cluster) instead of the traditional
way that from image tile with a fixed size. The InCNet (Inline Connection
Network) enriches the multi-scale connectivity without efficiency loss. The
proposal allows the input size enlarged to megapixel that can stitch the WSI by
the average repeats decreased from $10^3\sim10^4$ to $10^1\sim10^2$ for
collecting features and predictions at two scales. Based on Transformer for
classifying the integrated multi-scale multi-task WSI features, the
experimental results appear $0.872$ AUC score better than the best conventional
model on our dataset ($n$=2,019) from four scanners. The code is available at
https://github.com/Chrisa142857/You-Only-Look-Cytopathology-Once , where the
deployment version has the speed $\sim$70 s/WSI.
