Ji Tan

Area of research

Computer Vision and Pattern Recognition · Mechanical Engineering

Research interest

Research topics from publications: AANet: Adaptive Attention Network for COVID-19 Detection From Chest X-Ray Images; DBGANet: Dual-Branch Geometric Attention Network for Accurate 3D Tooth Segmentation; Large depth range binary-focusing projection 3D shape reconstruction via unpaired data learning; Structured light 3D shape measurement for translucent media base on deep Bayesian inference. Representative work: Accurate and rapid diagnosis of COVID-19 using chest X-ray (CXR) plays an important role in large-scale screening and epidemic prevention. Unfortunately, identifying COVID-19 from the CXR images is challenging as its radiographic features have a variety of complex appearances, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. To solve this problem, we propose an adaptive attention network (AANet), which can adaptively extract the characteristic radiographic findings of COVID-19 from the infected regions with various scales and appearances. It contains two main components: an adaptive deformable ResNet and an attention-based encoder. First, the adaptive deform Accurate segmentation of 3D dental models derived from intra-oral scanners (IOS) is one of the key steps in many digital dental applications such as orthodontics and implants. However, it is difficult to accurately segment individual teeth and gums in 3D dental models due to the following problems: 1) the shape and appearance of adjacent teeth are very similar, which is easy to be misidentified; 2) the boundary between teeth and gums is often indistinct, especially in orthodontic patients with abnormalities such as missing and crowded teeth. To solve such problems, a Dual-Branch Geometric Attention Network (DBGANet) for 3D tooth segmentation is proposed, which can capture tooth geometric str

Looking for a research collaborator?

Search millions of scientists by field, institution, impact, and funding status — see their work, find their email, and reach out directly.