DeepCIN: Attention-based cervical histology image classification with sequential feature modeling for pathologist-level accuracy - Sudhir Sornapudi; R Joe Stanley; William V Stoecker; Rodney Long; Zhiyun Xue; Rosemary Zuna; Shellaine R Frazier; Sameer Antani (Elsevier, 2020)
Background: 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. Methodology: 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 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 and (2) an attention-based fusion network image-level classifier predicting the final CIN grade by merging vertical segment sequences. Results: The model produces the CIN classification results and also determines the vertical segment contributions to CIN grade prediction. Conclusion: Experiments show that DeepCIN achieves pathologist-level CIN classification accuracy.