Collaborative Research: CISE-MSI: RCBP-RF: CNS: ESD4CDaT - Efficient System Design for Cancer Detection and Treatment

机器学习（ML）的应用变得越来越流行，不仅在医疗保健和医学领域，而且在其他领域解决真实的生活问题。ML推动来自不同领域的研究人员进行跨学科研究并获得新发现。两个机构的研究人员团队在Jarvis Christian University（JCU）（前身为Jarvis Christian College（JCC））和克莱顿州立大学（CSU）开展ML和物联网（IoT）在癌症检测、预测和治疗方面的应用研究方面的兴趣越来越大。为了实现两所大学研究人员的利益，建立了一个合作研究项目。该项目的主要目标是促进合作，并探索ML和IoT在癌症检测，预测和治疗中的应用的新研究方向。为了支持目标的推进并使团队受益，该项目旨在1）开发基于机器学习的新型算法，以构建检测，识别和预测癌细胞生长和治疗的软件，2）将新型机器学习算法与廉价的物联网硬件融合，以实现癌症研究的数据收集，3）开发基于智能相机的应用程序，用于视觉皮肤癌数据收集和分析，以及4）为两个合作机构提供本科和研究生课程项目或论文，以进行医学成像和癌症治疗应用ML和IoT的研究和培训。该项目的主要成果是：1）HBCU的学生和教师与主要少数民族服务机构之间的合作，2）研究数据收集和癌症诊断，预测和治疗的分析，3）本科生和研究生水平的课程进步在ML和物联网应用程序的软件设计领域，以及4）通过CISE-MSI核心资助赠款，为两个机构之间未来的持续合作研究和教育奠定更坚实的基础。该奖项部分由历史上的黑人学院和大学卓越研究（HBCU-EiR）和CISE少数民族服务机构（CISE MSI）研究扩展计划资助。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

The applications of Machine Learning (ML) become more popular for solving real life problems not only in healthcare and medicines but also in other fields. ML pushes investigators from different fields to conduct interdisciplinary research and obtain new discovery. There are mutually growing interests among team of researchers at two institutions in conducting research on the applications of ML and Internet of Things (IoT) for cancer detections, predictions, and treatments at Jarvis Christian University (JCU) (formerly Jarvis Christian College (JCC)) and Clayton State University (CSU). To realize the interests of the investigators from both universities, a collaborative research project is established.The primary goal of the project is to catalyze collaborations and explore new research directions in the applications of ML and IoT for cancer detections, predictions, and treatments. To support the advancement of the goal and benefit the team, the project aims to 1) develop novel ML based algorithms to build software to detect, identify, and predict cancer cell growths and treatments,  2) infuse novel ML algorithms with the inexpensive IoT hardware to enable data collection for the cancer research, 3) develop smart camera based apps for visual skin cancer data collections and analysis, and 4) offer undergraduate and graduate course projects or theses for the two collaborative institutions to conduct research and training in applied ML and IoT for medical imaging and cancer treatments. The major outcomes of the project are 1) collaboration among students and faculty of an HBCU with a predominantly minority serving institute, 2) research data collections and analysis for cancer diagnosis, prediction, and treatment, 3) curriculum advancements at undergraduate and graduate levels in the areas of software design for ML and IoT applications, and 4) build stronger foundations of continuing future collaborative research and education between the two institutions via CISE-MSI core funding grants. This award was funded in part by the Historically Black Colleges and University Excellence in Research (HBCU-EiR) and the CISE Minority Serving Institutions (CISE MSI) Research Expansion programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.