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。最新的技术进步使癌症研究人员能够更有效地检测疾病。数字载玻片扫描仪可生成组织样本的高分辨率图像，专业的人工智能软件工具可帮助医学和科学专家解读组织区域。然而，现有的检测癌症区域的软件工具进行癌症综合预测的能力有限，并且预测是基于手工制作的特征或深度学习生成的特征来执行的。该项目将开发一个开源的混合交互式机器学习软件工具，使病理学家能够在整个切片图像中快速、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，以便癌症研究人员可以轻松定义他们可以提取感兴趣特征的区域。拟议的混合交互式机器学习软件工具将使阿巴拉契亚等缺乏病理学家的地区受益。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到增强其他研究领域所需的异构数据分析扩展到气象学和生态学的强度变化的解释。拟议研究工作的成果也将促进K-12，本科生和研究生在当地和全国范围内对癌症研究的兴趣，该项目的目标是开发一个开源混合互动机器学习软件工具，使用从两个不同特征生成的混合特征，通过降维后的元素乘法提供更好的准确性。这两个不同的特征将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特征的低维空间来匹配。该奖项反映了NSF的法定使命，并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，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.