Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications

Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks. To provide a complete study, two different medical applications, with two different requirements, have been analyzed. The first application consists of HS images taken from neurosurgical operations; the second one presents HS images taken from dermatological interventions. While the main constraint for neurosurgical applications is the processing time, in other environments, as the dermatological one, other requirements can be considered. In that sense, energy efficiency is becoming a major challenge, since this kind of applications are usually developed as hand-held devices, thus depending on the battery capacity. These requirements have been considered to choose the target platforms: on the one hand, three of the most powerful Graphic Processing Units (GPUs) available in the market; and, on the other hand, a low-power GPU and a manycore architecture, both specifically thought for being used in battery-dependent environments.

高光谱成像（Hyperspectral (HS) imaging）作为一种非接触、非电离且无创的技术，已被证实适用于医学诊断。然而，此类图像所承载的信息量极为庞大，难以向外科医生实时提供病变边界相关信息。为此，高性能计算（High-Performance-Computing, HPC）平台成为必要的支撑手段。

本文针对五种不同的高性能计算平台展开对比测试，评估各平台在处理用于高光谱图像分类的空谱联合方法时的性能表现，并分析其主要优势与不足。为确保研究的完整性，本文选取了两种具有不同应用需求的医学场景展开分析：第一种场景取自神经外科手术中采集的高光谱图像，第二种则来自皮肤科手术中采集的高光谱图像。

神经外科场景的核心约束为处理时长，而皮肤科这类场景则需考虑其他需求。就此而言，能源效率正成为一项重大挑战——此类应用通常以手持设备形式开发，因此依赖电池续航能力。

本次测试的目标平台正是基于上述需求遴选而出：一方面选用了市场上三款性能顶尖的图形处理器（Graphic Processing Units, GPUs）；另一方面则选取了一款低功耗图形处理器以及一款专为依赖电池供电的场景设计的众核架构。