  Cervical cancer is one of the deadliest cancers affecting women globally.
Cervical intraepithelial neoplasia (CIN) assessment using histopathological
examination of cervical biopsy slides is subject to interobserver variability.
Automated processing of digitized histopathology slides has the potential for
more accurate classification for CIN grades from normal to increasing grades of
pre-malignancy: CIN1, CIN2 and CIN3. Cervix disease is generally understood to
progress from the bottom (basement membrane) to the top of the epithelium. To
model this relationship of disease severity to spatial distribution of
abnormalities, we propose a network pipeline, DeepCIN, to analyze
high-resolution epithelium images (manually extracted from whole-slide images)
hierarchically by focusing on localized vertical regions and fusing this local
information for determining Normal/CIN classification. The pipeline contains
two classifier networks: 1) a cross-sectional, vertical segment-level sequence
generator (two-stage encoder model) is trained using weak supervision to
generate feature sequences from the vertical segments to preserve the
bottom-to-top feature relationships in the epithelium image data; 2) an
attention-based fusion network image-level classifier predicting the final CIN
grade by merging vertical segment sequences. The model produces the CIN
classification results and also determines the vertical segment contributions
to CIN grade prediction. Experiments show that DeepCIN achieves
pathologist-level CIN classification accuracy.
