Accurate 3D shape measurement is crucial for surgical support and alignment in robotic surgery systems. Stereo cameras in laparoscopes offer a potential solution; however, their accuracy in stereo image matching diminishes when the target image has few textures. Although stereo matching with deep learning has gained significant attention, supervised learning requires a large dataset of images with depth annotations, which are scarce for laparoscopes.
View Article and Find Full Text PDFThis paper presents a model for generating expressive robot motions based on human expressive movements. The proposed data-driven approach combines variational autoencoders and a generative adversarial network framework to extract the essential features of human expressive motion and generate expressive robot motion accordingly. The primary objective was to transfer the underlying expressive features from human to robot motion.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2023
The use of 3D measurement in endoscopic images offers practicality in cancer diagnosis, computer-assisted interventions, and making annotations for machine learning training data. An effective approach is the implementation of an active stereo system, using a micro-sized pattern projector and an endoscope camera, which has been intensively developed. One open problem for such a system is the necessity of strict and complex calibration of the projector-camera system to precisely recover the shapes.
View Article and Find Full Text PDFImmune checkpoint inhibitors (ICIs) have shown superior clinical responses and significantly prolong overall survival (OS) for many types of cancer. However, some patients exhibit long-term OS, whereas others do not respond to ICI therapy at all. To develop more effective and long-lasting ICI therapy, understanding the host immune response to tumors and the development of biomarkers are imperative.
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