CRII: SCH: Developing a new hybrid interactive machine learning software tool for cancer researchers

CRII：SCH：为癌症研究人员开发一种新的混合交互式机器学习软件工具

• 批准号: 2409704
• 负责人: Sanghoon Lee
• 金额: $ 17.47万
• 依托单位: Kennesaw State University Research and Service Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2409704&HistoricalAwards=false
• 关键词: CRII; SCH; Developing; new; hybrid

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Recent advances in technology enable cancer researchers to detect the disease more effectively. Digital slide scanners produce very high-resolution images of tissue samples and specialized artificial intelligence software tools help medical and scientific experts in interpreting the tissue area. However, existing software tools detecting cancer areas have a limited capability to conduct integrated prediction of the cancer and the prediction has been performed based on either through hand-crafted features or deep learning generated features. This project will develop an open-source hybrid interactive machine learning software tool that will enable pathologists to interactively detect cancer areas promptly and with high accuracy in the whole slide images. This will be done by fusing hand-crafted features identified by clinical researchers and deep learning generated features so that cancer researchers can easily define the regions within which they can extract features of interest. The proposed hybrid interactive machine learning software tool will benefit regions such as Appalachia, which have a lack of pathologists. The software tool developed will eventually reduce the cost of cancer diagnosis and treatment. Moreover, the successful accomplishment of the proposed research work can affect the enhancement of other research areas needed for heterogeneous data analysis extending to meteorology and ecology in the interpretation of intensity variation. The accomplished results of the proposed research works will also promote the interest in cancer research in K-12, undergraduate, and graduate locally and nationally, through synergistic research and education activities.The goal of this project is to develop an open-source hybrid interactive machine learning software tool using hybrid features generated from two distinct features providing better accuracy by element-wise multiplication after dimensionality reduction. The two distinct features will be matched by transforming the high dimensional space of deep learning generated features into the low dimensional space of hand-crafted features. The element-wise multiplication will maintain the histologic feature information of both hand-crafted features and deep learning generated features.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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助（公法117-2）。技术的进步使癌症研究人员能够更有效地检测到该疾病。数字幻灯片扫描仪可生产非常高分辨率的组织样品图像，专门的人工智能软件工具可帮助医学和科学专家解释组织区域。但是，现有的软件工具检测到癌症区域的能力有限，可以通过手工制作的功能或深度学习生成的特征来对癌症进行综合预测。该项目将开发一种开源混合互动机器学习软件工具，该工具将使病理学家能够在整个幻灯片图像中及时及时互动检测癌症区域。这将通过融合临床研究人员确定的手工制作的功能和深度学习产生的功能来完成，以便癌症研究人员可以轻松地定义他们可以提取感兴趣的特征的区域。拟议的混合交互式机器学习软件工具将使缺乏病理学家的阿巴拉契亚等地区受益。开发的软件工具最终将降低癌症诊断和治疗的成本。此外，拟议的研究工作的成功完成可能会影响在解释强度差异时扩展到气象和生态的异质数据分析所需的其他研究领域。拟议的研究工作的成就结果还将通过协同研究和教育活动来促进对K-12，本科和全国研究生和全国研究生的兴趣。该项目的目的是开发一种开放式混合交互式机器学习软件工具，使用开放式开放式交互式机器学习软件工具，使用从元素通过元素确定的两种精确精确的功能产生的混合功能，可通过元素构成元素的乘积乘以增量。通过将深度学习生成的特征的高维空间转换为手工制作的功能的低维空间，将匹配这两个不同的功能。元素的乘法将维持两个手工制作的功能和深度学习生成的功能的组织学特征信息。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。