Novel region-based image compression method based on spiking cortical model

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

Deep Learning Blockchain Integration Framework for Ureteropelvic Junction Obstruction Diagnosis Using Ultrasound Images

UreteroPelvic Junction Obstruction(UPJO)is a common hydronephrosis disease in children that can result in an even progressive loss of renal function.Ultrasonography is an economical,radiationless,noninvasive,and high noise preliminary diagnostic step for UPJO.Artificial intelligence has been widely applied to medical fields and can greatly assist doctors'diagnostic abilities.The demand for a highly secure network environment in transferring electronic medical data online,therefore,has led to the development of blockchain technology.In this study,we built and tested a framework that integrates a deep learning diagnosis model with blockchain technology.Our diagnosis model is a combination of an attention-based pyramid semantic segmentation network and a discrete wavelet transformation-processed residual classification network.We also compared the performance between benchmark models and our models.Our diagnosis model outperformed benchmarks on the segmentation task and classification task with MloU=87.93,MPA=93.52,and accuracy=91.77%.For the blockchain system,we applied the InterPlanetary File System protocol to build a secure and private sharing environment.This framework can automatically grade the severity of UPJO using ultrasound images,guarantee secure medical data sharing,assist in doctors'diagnostic ability,relieve patients'burden,and provide technical support for future federated learning and linkage of the Internet of Medical Things(loMT).