GPU-Compute Server

我们正在申请资金，以购买必要的硬件，为基于图像的综合医学研究中的人工智能（AI）建立基础设施。将建立的基础设施包括高性能和高效的基于gpu的计算系统，这是开发基于图像的医学科学（放射学和病理学）的人工智能方法验证所必需的。与之相关联的存储系统，可以高速提供训练数据。这种人工智能系统对于放射学和病理学的数据分析至关重要，因为通常需要处理和分析非常大的数据集（大数据）。我们目前每年产生大约600 TB的数据（病理学、放射学、分子病理学）。这种多维数据的手工（临床）评估正变得越来越复杂和耗时。同时，该图像数据对于各自的疾病亚型和由此产生的个体化、高度特异性的治疗方案具有非常高的信息含量。人工智能支持的对这些数据的自动化分析已经显示出巨大的成功，帮助医生做出更准确的诊断，提高工作效率。此外，人工智能还可以发现新的生物标志物，从而能够早期诊断癌症等疾病，或者通过更精确的亚型分型来改善患者的个体治疗和护理。有了所需的基础设施，我们希望深化这些研究领域，研究医学图像数据中的更深层次的模式，从而有助于更好的患者护理和更好地理解病理机制。到目前为止，这个研究领域还没有现成的人工智能基础设施。

We are applying for funds to procure the necessary hardware to set up an infrastructure for artificial intelligence (AI) in integrated image-based medical research. The infrastructure to be set up consists of a high-performance and efficient GPU-based compute system, which is required for the development of validation of AI methods in image-based medical sciences (radiology and pathology). An associated storage system is connected to it, that can provide the training data at high speed. This AI system is essential for data analysis in radiology and pathology, as very large data sets often have to be processed and analyzed (big data). We currently produce around 600 TB of data (pathology, radiology, molecular pathology) annually. The manual (clinical) evaluation of this multidimensional data is becoming increasingly complex and time-consuming. At the same time, this image data potentially has a very high information content for the respective disease subtypes and the resulting individualized, highly specific therapy options. The automated, AI-supported analysis of this data is already showing great success in helping doctors to make more accurate diagnoses and work more efficiently. Furthermore, new biomarkers can be found with AI, which enable the early diagnosis of diseases such as cancer or improve the individual therapy and care of patients through more precise subtyping. With the requested infrastructure, we want to deepen these research areas, investigate deeper patterns in the medical image data and thus contribute to better patient care and a better understanding of the mechanisms of pathologies. So far, there is no existing AI infrastructure for this research area.