This work presents a contribution to advance current solutions for the problem of melanoma detection based on deep learning (DL) approaches. This is an active research field, which aims to aid on the detection and classification of melanoma (the most lethal type of skin cancer) with non-invasive solutions. By exploiting both 2D and 3D characteristics of the skin lesion surface, the proposed approach advances beyond commonly used colour features of dermoscopic images. Two competing classification methods are exploited, namely Multiple Instance Learning (MIL) and DL, which are combined using an uncertaintyaware decision function. The DL method performs classification resorting to RGB data, while MIL performs 3D feature extraction, selects the most significant set, and performs classification at two different learning instances. The novel aspects of this work include DL uncertainty evaluation mechanisms along with MIL to train a robust ensemble classifier, and also the use of dense light-fields for skin lesion classification. Despite the large class imbalance (often present in medical image datasets), the ensemble model achieves crossvalidated melanoma classification accuracy of 84.00% when trained against nevus lesions, and 90.82% accuracy when discriminating against all present lesion types. The results show that, in the absence of discriminative 2D characteristics, the 3D surface provides redeeming results, demonstrating that existing methods can benefit from the proposed method by looking beyond 2D image characteristics.
This paper studies the performance and energy consumption of several multi-core, multi-CPUs and manycore hardware platforms and software stacks for parallel programming. It uses the Multimedia Multiscale Parser (MMP), a computationally demanding image encoder application, which was ported to several hardware and software parallel environments as a benchmark. Hardware-wise, the study assesses NVIDIA's Jetson TK1 development board, the Raspberry Pi 2, and a dual Intel Xeon E5-2620/v2 server, as well as NVIDIA's discrete GPUs GTX 680, Titan Black Edition and GTX 750 Ti. The assessed parallel programming paradigms are OpenMP, Pthreads and CUDA, and a single-thread sequential version, all running in a Linux environment. While the CUDA-based implementation delivered the fastest execution, the Jetson TK1 proved to be the most energy efficient platform, regardless of the used parallel software stack. Although it has the lowest power demand, the Raspberry Pi 2 energy efficiency is hindered by its lengthy execution times, effectively consuming more energy than the Jetson TK1. Surprisingly, OpenMP delivered twice the performance of the Pthreads-based implementation, proving the maturity of the tools and libraries supporting OpenMP.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.