Cervical intraepithelial neoplasia acetic acid white images - pre-cancerous lesion three-class classification

Cervical cancer ranks first in incidence among malignant tumors of the
female reproductive system, and 80% of women who die from cervical cancer
worldwide are from developing countries. VIA screening based on artificial
intelligence-assisted diagnosis can provide a cheap and rapid screening
method. This will attract more low-income women to volunteer for regular
cervical cancer screening. However, current AI-based VIA screening studies
either have low accuracy or require expensive equipment assistance. In
this paper, we propose the HMCFormer (Hierarchical Multi-Scale
Convolutional Transformer) network, which combines the hierarchical
feature extraction capability of CNNs and the global dependency modeling
capability of Transformers to address the challenges of realizing
intelligent VIA screening. HMCFormer can be divided into a Transformer
branch and a CNN branch. The Transformer branch receives unenhanced lesion
sample images, and the CNN branch receives lesion sample images enhanced
by the proposed dual-color space-based image enhancement algorithm. The
authors design a Hierarchical Multi-Scale Pixel Excitation (HMSPE) module
for adaptive multi-scale and multi-level local feature extraction. The
authors apply the structure of the Swin Transformer network with minor
modifications in the global perception modeling process. In addition, the
authors propose two feature fusion concepts: adaptive preprocessing and
superiority-inferiority fusion, and design a feature fusion module based
on these concepts, which significantly improves the collaborative ability
of the Transformer branch and the CNN branch.