Publications by authors named "R Sagawa"

Article Synopsis
  • Researchers are developing a method to create 3D shapes from images that retains accurate scales, addressing a common issue in current techniques that only produce relative depth maps.
  • They create a dataset of synchronized RGB images and depth maps using an endoscope simulator to train a depth prediction network.
  • The trained network predicts depth from RGB images, and this depth information is used to align and reconstruct a 3D shape, which is then validated with actual endoscope images.
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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.

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This 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.

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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.

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Immune 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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