RESUMEN
Colposcopy is an essential examination tool to identify cervical intraepithelial neoplasia (CIN), a precancerous lesion of the uterine cervix, and to sample its tissues for histological examination. In colposcopy, gynecologists visually identify the lesion highlighted by applying an acetic acid solution to the cervix using a magnifying glass. This paper proposes a deep learning method to aid the colposcopic diagnosis of CIN by segmenting lesions. In this method, to segment the lesion effectively, the colposcopic images taken before acetic acid solution application were input to the deep learning network, U-Net, for lesion segmentation with the images taken following acetic acid solution application. We conducted experiments using 30 actual colposcopic images of acetowhite epithelium, one of the representative types of CIN. As a result, it was confirmed that accuracy, precision, and F1 scores, which were 0.894, 0.837, and 0.834, respectively, were significantly better when images taken before and after acetic acid solution application were used than when only images taken after acetic acid solution application were used (0.882, 0.823, and 0.823, respectively). This result indicates that the image taken before acetic acid solution application is helpful for accurately segmenting the CIN in deep learning.
RESUMEN
Graphene exhibits unusual electronic properties, caused by a linear band structure near the Dirac point. This band structure is determined by the stacking sequence in graphene multilayers. Here we present a novel method of microscopically controlling the band structure. This is achieved by epitaxy of graphene on 3C-SiC(111) and 3C-SiC(100) thin films grown on a 3D microfabricated Si(100) substrate (3D-GOS (graphene on silicon)) by anisotropic etching, which produces Si(111) microfacets as well as major Si(100) microterraces. We show that tuning of the interface between the graphene and the 3C-SiC microfacets enables microscopic control of stacking and ultimately of the band structure of 3D-GOS, which is typified by the selective emergence of semiconducting and metallic behaviours on the (111) and (100) portions, respectively. The use of 3D-GOS is thus effective in microscopically unlocking various potentials of graphene depending on the application target, such as electronic or photonic devices.