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.

翻译：高光谱成像作为一种非接触、无电离、无创的技术，已被证明适用于医学诊断。然而，这类图像中包含的海量信息使得实时为外科医生提供边界信息面临挑战。为此，高性能计算平台显得不可或缺。本文对五种不同高性能计算平台在处理高光谱图像时空分类方法时的性能进行了对比，评估了它们的主要优势与局限。为进行完整研究，我们分析了两种具有不同需求的医学应用：第一种应用涉及神经外科手术中的高光谱图像；第二种涉及皮肤科干预中的高光谱图像。对于神经外科应用，主要限制因素是处理时间；而在皮肤科等其他环境中，则可考虑其他要求。在此背景下，能效正成为重大挑战——此类应用通常以手持设备形式开发，依赖电池容量。基于这些需求，我们选择了目标平台：一方面是市场上三种最强大的图形处理单元（GPU）；另一方面是低功耗GPU与多核架构，两者均专为电池依赖环境设计。