Edge segmentation: Empowering mobile telemedicine with compressed cellular neural networks

With the need for increased care and welfare of the rapidly aging population, mobile telemedicine is becoming popular for providing remote health care to increase the quality of life. Recently, image analysis is being actively applied for medical diagnosis and treatment, in which image segmentation is of the fundamental importance for other image processing such as visualization and detection. However, given the tasks challenges in transmitting large volume of high-resolution images and the real-time constraints that are commonly present for mobile telemedicine, image segmentation is best done at the “edge”, i.e., locally so that only segmentation results are communicated. A powerful approach to medical image segmentation is cellular neural network (CeNN), which can achieve very high accuracy through proper training. However, CeNNs typically involve extensive computations in a recursive manner. As an example, to simply process an image of 1920×1080 pixels requires 4–8 Giga floating point multiplications (for 3×3 templates and 50–100 iterations), which needs to be done in a timely manner for real-time medical image segmentation. Such a demand is too high for most low power mobile computing platforms in IoTs, This paper presents a compressed CeNN framework for computation reduction in CeNNs, which is the first in the literature. It involves various techniques such as early exit and parameter quantization, which significantly reduces computation demands while maintaining an acceptable performance.