In CB-XLCT imaging, when nanophosphors are delivered to tumor tissues,irradiated by X-rays, luminescent light with wavelength range of 500-700 nm will be exciteddetected by optical camera from different directions. After reconstruction, it can provide tumor locations. The micro-CT subsystem also provide X-rays to excite nanoparticles for luminescence tomography.

Similar to the critical point tracking technology of human vision, the split attentionspatial grouping enhancement module, combining the multi-path grouping architecturespatial attention technology, can accurately extract important information from the imageimprove the network performance. Moreover, adopting neural architecture search technology to automatically search for the most suitable network architecture based on current moduleshardware devices can balance diagnosis speedhigh accuracy.

The HeaortaNet is developed by the TW-CVAI team. The HeaortaNet is a deep learning model trained by 70,000 axial images from 200 patients with verified annotations of the pericardiumaorta. It shortens the time for data processing from 60 minutes, by manual segmentation of both pericardiumaorta, to 0.4 seconds. The segmentation accuracy, as assessed by dice similarity coefficient, is 94.8 for the pericardium,91.6 for the aorta. The imaging-based Cardiovascular Risk Prediction module was constructed by analyzing data from the National Health Insurance Databank.

In order to develop an AI model that can accuratelycompletely segment coronary arteries, our team, the TW-CVAI, has established a training dataset composed of strictly verified annotations of coronary lumen boundaries in coronary CT angiography (CCTA). We designed a deep learning model, two-channel 3D-UNet, with a priori prerequisite (vesselness prior) to facilitate identification of vascular structures. The final model, the TaiCAD-Net, greatly shortens the CCTA interpretation time from 6 hours to 10 minutes, with the overall segmentation accuracy of 86 by Dice similarity coefficient.

The proposed technologies apply deep learning methodsbig clinical data for lung cancer decision support. The system consists of: 1) CT radiogenomicspatho-genomics for automatic cancer detectionprediction of EGFR mutation 2) demographics to predict survival 3) genomics to predict cancer recurrencemetastasis,4) drug response inference for the best target therapy option. The modules are constructed on an AI-based Clinical Decision Support System (CDSS-SDM). The system is expected to provide physicianspatients with personalized medication recommendations.

The computer-aided nodule detection system in CT image consists of the search sliding window, YOLOv2 architecture, 3-D CapsNet, skip connection,post-processing. First, the CT image is divided into numerous VOIs by sliding window. Second, a 3-D CapsNet based on YOLOv2 architectureskip connection is applied to the VOIs for classifying VOIs as nodulenot. Finally, the non-maximum suppression algorithm is performed to decide the final detection result.

PANCREASaver contains a “PC automatic segmentation model” (image segmentation)a “PC analysis AI model” (image classification) that can read the DICOM format of postcontrast CT images directly for the automatic analysis process. After conducting prep-processing with image processing algorithms, C2FNAS is employed to illustrate the tumor position prior to the diagnosis conducted by CNN. The results can be provided to the physician for diagnostic reference so as to reduce early omissionsincrease the detection rate of pancreatic cancer.