4 accepted papers
The message passing–readout framework has become the de facto standard of graph neural networks (GNNs) for material property prediction. However, most existing readout functions are built on an atom-decomposable inductive bias, i.e. the material-level property or feature can be reasonably assigned t…
Tumor segmentation is particularly important for ultrasound imaging-based diagnosis and therapy, such as breast cancer and gastrointestinal stromal tumor. However, the accurate ultrasound tumor segmentation remains challenging due to insufficient textures and edge features resulted from limited reso…
Monocular endoscopic depth estimation is essential for surgical navigation. Current deeply learned estimation methods still suffer from lack of real data labels and porous, artifacts (e.g., bubbles), illumination variations (e.g., specular highlight), and weak texture in endoscopic video images. Thi…
Automatic segmentation of pulmonary arteries and veins in CT has great clinical significance. Because the growth range of a single vessel is vast, and the arteries and veins have barely identical intensity values on CT and grow very close to or even interleaved, accurate segmentation of them require…