Deep Learning

“MeDA OXR: Real-time Hazard Recognition System,” which can automatically screeninterpret the medical images in real-time, is designed for emergency rooms to assist physicians in diagnosis, reducing medical risks,improving overall efficiency. Combining portable X-RayAI algorithms, the system performs real-timeaccurate preliminary screeningdiagnoses of diseases, such as pneumothorax, pneumonia,tuberculosis. It can also locate the nasogastric tube, endotracheal tube,central venous catheter, while misplacement of that is sent to the physicians when detected.

This invention presents a "next-generation augmented reality navigation system” using the generative adversarial network-long short term memory network (GAN-LSTM) framework with integrated GPS module to implement a novel AR navigation system. Unlike the presented AR navigation system, the virtual guided path is "autonomously generated" in captured image rather than superimpose on the image by using the pre-rendered 3D content, which not only provide a more authenticcorrect AR effect to user but also earlycorrectly guide the driver when driving in complex road traffic environment.

"The IHC staining intensity grading algorithm combines a series of complementary processes of deep learningimage processing in order to overcome the difficulties in cell boundary segmentationgrade definition,provides a cell-based IHC staining intensity gradingquantification capability. The proposed method serves as a useful assistive tool for physicians in performing accurate staining quantification in tissue microscopic images. In addition, it analyzes panoramic pathological images in order to determine the factors most strongly associated with likely disease recurrence."

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.

"1.Background: The deterioration of lithium batteries leads to safetycost issues. Ultrasonic detection has the advantages of cost, size,immediacy. 2.Detection: Under the control environment, the ultrasonic signal is actuated on each part of the battery,the reflectedtransmitted wave is collected. 3.Analysis: It has methods such as spectrum, image,DL to evaluate the SoH, the distribution of degradation types,the location of faulty cells in the battery module. 4.Theory: Use the acoustic properties of the battery with software simulation to verify the measured data."