Histopathology analysis of thyroid nodule is the current gold standard for the differential diagnosis of thyroid tumors. Deep learning methods have been extensively used for the diagnosis of histopathology images. We look into the possibility of the differential diagnosis of thyroid tumors by analysing histopathology images of thyroid nodule capsules using different deep learning methods. Residual Network (ResNet), Densely Connected Network (DenseNet) and Vision Transformer (ViT). Our study shows the superiority of the histopathology images of thyroid nodule capsules for the differential diagnosis of thyroid tumors compared to histopathology images of thyroid nodules.
Scattering-type Scanning Near Field Optical Microscopy (s-SNOM) has been demonstrated as a valuable tool for revealing important properties of materials at nanoscale. Recent proof-of-concept experiments have shown that, among others, s-SNOM can provide quantitative information on the real and imaginary parts of the dielectric function, and hence of intrinsic optical properties of materials and biological samples. In this work we further explored these capabilities in several experiments dealing with microcapsules for drug delivery, ultra-thin optical coatings with tunable color properties, and two types of nanoparticles with important applications in energy storage and conversion, or biosensing and theranostics.
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