SBIR Phase I: Exploring bias in Deep learning to extend its use to under-represented populations in breast imaging

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将来自于部署人工智能（AI）的能力，以实现乳房x线照片中乳腺癌的快速和自动检测，同时还有一个额外的好处，即对各种人口统计数据的模型系统进行更多的培训，其中可变性可能会导致错误或无意的偏差增加。乳腺癌是妇女中最常见的癌症，也是妇女癌症死亡的第二大原因。x光乳房x光检查是降低女性死亡率的有效工具，但每年产生的大量乳房x光检查，加上在复杂背景下识别细微异常的视觉挑战，使得乳房x光检查分析成为一项艰巨的任务。该项目提出了一个基于人工智能的系统，能够自主分析乳房x线照片，以帮助放射科医生完成这项任务，拥有涵盖多种人口统计数据的广泛数据集。提出的解决方案将节省放射科医生的时间，使医疗保健系统更有效地照顾妇女，同时提供高精度和减少解释的可变性。提出的解决方案可能会导致更广泛的乳房x光检查项目的坚持，显著降低乳腺癌死亡率，并为社会节省大量成本。此外，该项目可能会改善其他类型癌症的治疗方法。这项小企业创新研究（SBIR）第一阶段项目建议开发一种算法，以减少乳房成像人工智能方法中的偏见，从而将其应用于代表性不足的人口群体。由于测试和训练数据的来源有限，目前可用于训练人工智能模型的大多数乳房x光检查数据库在代表性不足的人群中可能训练不足，这可能导致在其他人群中使用时出现错误或准确性降低。该项目提出了一个数据增强程序，通过综合增加代表性不足的人口统计数据的训练示例数量来减少偏见。首先，将从现有数据库中提取人口统计信息，并确定偏倚因素。然后，将建立一个新的程序，并用于生成高度逼真的合成乳房x线照片，以增加原始数据库中训练示例的数量。通过在原始数据库和增强数据库上训练人工智能，并在接收操作员特征下的面积方面比较性能，从而评估该程序获得的收益。在第一阶段项目完成后，将准备好扩大程序，以便在代表性不足的少数群体中进行测试。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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the ability to deploy Artificial Intelligence (AI) to enable rapid and automated detection of breast cancers in mammograms, with the added benefit of greater training of the systems of models on data across a range of demographics, where variability could potentially cause increased errors or inadvertent bias. Breast cancer is the most commonly diagnosed cancer in women and the second leading cause of cancer deaths among women. X-ray mammography screening is an effective tool for reducing mortality among women, but the high volume of mammograms generated every year, combined with the visual challenges of identifying subtle abnormalities in a complex background, makes mammogram analysis a difficult task. This project proposes an AI-based system able to autonomously analyze mammograms to help radiologists in this task, with a broad dataset covering multiple demographics. The proposed solution will save the time of radiologists, allowing the healthcare system to care for women more efficiently, while providing high accuracy and reducing variability of interpretation. The proposed solution may result in a broader adherence to mammography screening programs, marked reduction in breast cancer mortality, and significant cost savings for society. Additionally, this project may lead to improved treatments of other types of cancer.This Small Business Innovation Research (SBIR) Phase I project proposes to develop an algorithm to reduce bias in AI methods for breast imaging in order to extend their use to under-represented demographic groups. Most mammogram databases currently available to train AI models are potentially undertrained for use in under-represented populations because of the limited origin of the test and training data, potentially leading to errors or reduced accuracy when used on other populations. This project proposes a data augmentation procedure to reduce bias by synthetically increasing the number of training examples for under-represented demographics. Firstly, demographic information will be extracted from available databases, and biasing factors will be identified. Then, a new procedure will be established and used to generate highly realistic synthetic mammograms to increase the number of training examples in the original database. The benefits obtained with this procedure will be assessed by training an AI on the original and on the augmented databases and comparing performances in terms of area under the receiving operator characteristic. At the completion of this Phase I project, the augmentation procedure will be ready for testing on under-represented minorities.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.