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

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。最近的技术进步使癌症研究人员能够更有效地发现这种疾病。数字切片扫描仪可以产生非常高分辨率的组织样本图像，专门的人工智能软件工具可以帮助医学和科学专家解释组织区域。然而，现有的检测癌症区域的软件工具对癌症进行综合预测的能力有限，预测要么是基于手工制作的特征，要么是基于深度学习生成的特征。该项目将开发一种开源混合交互式机器学习软件工具，使病理学家能够在整个幻灯片图像中快速、高精度地交互式检测癌症区域。这将通过融合临床研究人员识别的手工特征和深度学习生成的特征来实现，这样癌症研究人员就可以轻松定义区域，从中提取感兴趣的特征。提出的混合交互式机器学习软件工具将使阿巴拉契亚等地区受益，这些地区缺乏病理学家。开发的软件工具最终将降低癌症诊断和治疗的成本。此外，该研究工作的成功完成可以影响异质性数据分析在强度变化解释方面需要扩展到气象和生态学的其他研究领域的加强。拟议研究工作的完成结果还将通过协同研究和教育活动，促进本地和全国K-12、本科生和研究生对癌症研究的兴趣。这个项目的目标是开发一个开源的混合交互式机器学习软件工具，使用由两个不同特征生成的混合特征，在降维后通过元素明智的乘法提供更好的准确性。通过将深度学习生成特征的高维空间转换为手工制作特征的低维空间，将两个不同的特征进行匹配。元素智能乘法将保留手工特征和深度学习生成特征的组织学特征信息。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.