A Whole Slide Image Grading Benchmark and Tissue Classification for Cervical Cancer Precursor Lesions with Inter-Observer Variability
This work addresses the challenge of inconsistent diagnoses in cervical cancer screening, which is crucial for improving early detection and treatment, though it appears incremental as it builds on existing grading conventions.
The paper tackles the problem of grading cervical cancer precursor lesions from whole-slide images by developing a tissue classification method that handles papillae and overlapping cells, achieving performance evaluated with concrete metrics, and it introduces a benchmark to reveal inter-observer variability among pathologists.
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.