Feature Library: A Benchmark for Cervical Lesion Segmentation

Cervical cancer causes the fourth most cancer-related deaths of women worldwide. One of the most commonly-used clinical tools for the diagnosis of cervical intraepithelial neoplasia (CIN) and cervical cancer is colposcopy examination. However, due to the challenging imaging conditions such as light reflection on the cervix surface, the clinical accuracy of colposcopy examination is relatively low. In this paper, we propose a computer-aided diagnosis (CAD) system to accurately segment the lesion areas (i.e., CIN and cancer) from colposcopic images, which can not only assist colposcopists for clinical decision, but also provide the guideline for the location of biopsy sites. In clinical practice, colposcopists often need to zoom in the potential lesion area for clearer observation. The colposcopic images with multi-scale views result in a difficulty for current straight-forward deep learning networks to process. To address the problem, we propose a novel attention mechanism, namely feature library, which treats the whole backbone network as a pool of features and extract the useful features on different scales from the pool to recalibrate the most informative representation. Furthermore, to well-train and evaluate our deep learning network, we collect a large-scale colposcopic image dataset for CervIcal lesioN sEgMentAtion (CINEMA), consisting of 34,337 images from 9,652 patients. The lesion areas in the colposcopic images are manually annotated by experienced colposcopists. Extensive experiments are conducted on the CINEMA dataset, which demonstrate the effectiveness of our feature library dealing with cervical lesions of varying sizes.