A Fault Tolerant Data Structure for Peer-to-Peer Range Query Processing

Abstract We present a fault tolerant dynamic data structure based on a constant-degree Distributed Hash Table (DHT) called FissionE that supports orthogonal range search in d-dimensional space. A publication algorithm, which distributes data objects among all nodes in the network is described, along with a search algorithm that processes range queries and reports all objects in range to the query issuer. Routing messages between two nodes is performed by the FissionE routing algorithm. The worst case orthogonal range search cost in our data structure with n nodes is O(log n+m) messages plus reporting cost, where m is the minimum number of nodes intersecting the query. Storing d complete copies of each data object on d different nodes provides redundancy for our scheme. This redundancy permits completely answering a query in the case of simultaneous failure of d − 1 nodes

A Survey on Deep Learning for Skin Lesion Segmentation

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online at https://github.com/sfu-mial/skin-lesion-segmentation-survey.Comment: Published in Medical Image Analysis (2023); 55 pages, 10 figures; Mirikharaji and Abhishek: Joint first authors; Celebi and Hamarneh: Joint senior author