Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning. Many BUS segmentation approaches have been studied in the last two decades, and have been proved to be effective on private datasets. Currently, the advancement of BUS image segmentation seems to meet its bottleneck. The improvement of the performance is increasingly challenging, and only few new approaches were published in the last several years. It is the time to look at the field by reviewing previous approaches comprehensively and to investigate the future directions. In this paper, we study the basic ideas, theories, pros and cons of the approaches, group them into categories, and extensively review each category in depth by discussing the principles, application issues, and advantages/disadvantages. Keyword: breast ultrasound (BUS) images; breast cancer; segmentation; benchmark; early detection; computer-aided diagnosis (CAD) IntroductionBreast cancer occurs in the highest frequency in women among all cancers, and is also one of the leading causes of cancer death worldwide [1,2]. Scientists do not definitely know what causes breast cancer yet, and only know some risk factors that can increase the likelihood of developing breast cancer: getting older, genetics, radiation exposure, dense breast tissue, alcohol consumption, etc. The key of reducing the mortality is to find signs and symptoms of breast cancer at its early stage by clinic examination [3]. Breast ultrasound (BUS) imaging has become one of the most important and effective modality for the early detection of breast cancer because of its noninvasive, nonradioactive and cost-effective nature [4]; and it is most suitable for large-scale breast cancer screening and diagnosis in low-resource countries and regions. processing approaches. The category of the others [136,138,139,140,[142][143][144][145][146] is composed of three small sub-categories, each contains only few literatures. Due to the challenging nature of the task, just using single image processing technique cannot achieve desirable results; and most successful approaches employ hybrid techniques and model biological priors.The rest of the paper is organized as follows: in section 2, the fundamental issues in BUS segmentation are discussed, e.g., denoising, interaction, biological priors modeling, validation, and the possible problemsolving strategies; in sections 3 -6, we review automatic BUS image segmentation methods by presenting the principle of each category, discussing their advantages and disadvantages, and summarizing the most valuable strategies. In section 7, we discuss the approaches of three sub-categories briefly. Section 8 gives the conclusion and the future directions. Fundamental Issues of BUS Image SegmentationBUS segmentation approaches have been studied in the last two decades extensively, and many of them achieved good performances utilizin...

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Design of low-complexity FIR filters based on signed-powers-of-two coefficients with reusable common subexpressions

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GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference; (2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies.

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