# A Whole Slide Image Grading Benchmark and Tissue Classification for Cervical Cancer Precursor Lesions with Inter-Observer Variability

## TL;DR

This paper introduces a comprehensive benchmark for grading cervical cancer precursor lesions using whole-slide images, addressing inter-observer variability and proposing a novel tissue classification method based on morphological features.

## Contribution

It presents a new whole-slide image grading benchmark, a tissue classification method utilizing a novel morphological feature, and an analysis of inter-observer variability among pathologists.

## Key findings

- The tissue classification method achieved promising accuracy.
- Inter-observer variability significantly impacts diagnosis consistency.
- The benchmark facilitates standardized evaluation of grading algorithms.

## Abstract

The cervical cancer developing from the precancerous lesions caused by the Human Papilloma Virus (HPV) has been one of the preventable cancers with the help of periodic screening. There are two types of grading conventions widely accepted among pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is introduced. The papillae of the cervical epithelium and overlapping cell problems are handled and a tissue classification method with a novel morphological feature exploiting the relative orientation between the BM and the major axis of all nuclei is developed and its performance is evaluated. Besides, the inter-observer variability is also revealed by a thorough comparison among pathologists' decisions, as well as, the final diagnosis.