/PRNewswire/ Bairong Inc ("Bairong" or "the Company", 6608.HK), a leading independent AI-powered technology platform in China, was recently granted three.
/PRNewswire/ Bairong Inc ("Bairong" or "the Company", 6608.HK), a leading independent AI-powered technology platform in China, was recently granted three.
/PRNewswire/ Google Doodle has honored and celebrated the late Prof. Lotfi Zadeh (of UC Berkeley), the world-renowned mathematician, computer scientist, and.
Purpose: To investigate multiple deep learning methods for automated segmentation (auto-segmentation) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI). Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time-consuming task, in which variability between observers may directly impact on patient treatment outcomes. Auto-segmentation on computed tomography imaging has been shown to result in significant time reductions and more consistent outlines of the organs at risk. Methods: Three convolutional neural network (CNN)-based auto-segmentation architectures were developed using manual segmentations and T2-weighted MRI images provided from the American Association of Physicists in Medicine (AAPM) radiotherapy MRI auto-contouring (RT-MAC) challenge dataset (n = 31). Auto-segmentation performance was evaluated with segmentation similarit
Diabetic Foot Ulcer is a very common problem that faces diabetic patients with almost 15 percent of these patients developing a foot ulcer at least once in their life. Diabetic patients tend to suffer from numbness and loss of sensation in their feet making it hard for them to self-detect the ulcer therefore an early detection method is needed. The approach conducted in this paper uses a 2-dimentional image as an input and using convolutional neural networks to analyze the images. The system will classify the input images into two states, no ulcer or the ulcer is present and in this case the location of the ulcer will be marked on the image. The system achieved an F1- score of 81.3% with an improvement of more than 7% from the F1-score achieved in the Diabetic Foot Ulcer Challenge 2020.