Publications by authors named "Yu-Huan Chang"
Article Synopsis
- Malignant melanoma is a serious skin cancer that is becoming more common, prompting the need for effective early detection methods.
- Previous studies mostly focused on dermoscopic images, but this research uses a convolutional neural network (CNN) to analyze optical coherence tomography (OCT) images of mouse skin for better melanoma identification.
- The CNN shows impressive accuracy with a sensitivity of 0.99 and specificity of 0.98, providing a risk score for melanoma presence that could help improve early diagnosis and treatment in clinical practices.
This study develops a handheld optical coherence tomography angiography (OCTA) system that uses a high-speed (200 kHz) swept laser with a dual-reference common-path configuration for stable and fast imaging. The common-path design automatically avoids polarization and dispersion mismatches by using one circulator as the primary system element, ensuring a cost-effective and compact design for handheld probe use. With its stable envelope (i.
View Article and Find Full Text PDFMelanoma is a high-risk skin cancer because it tends to metastasize early and ultimately leads to death. In this study, we introduced a noninvasive multifunctional optical coherence tomography (MFOCT) for the early detection of premetastatic pathogenesis in cutaneous melanoma by label-free imaging of microstructures (i.e.
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