SBIR Phase II: Advanced Computer Vision Methods for Diagnostic Medical Entomology

SBIR 第二阶段：用于诊断医学昆虫学的先进计算机视觉方法

• 批准号: 2322335
• 负责人: Autumn Goodwin
• 金额: $ 99.95万
• 依托单位: VECTECH, LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322335&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Advanced; Computer

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to enable the provision of high quality vector surveillance data to public health institutions domestically and internationally. Vectors, or organisms that transmit diseases to other organisms, like mosquitoes and ticks, have a significant impact on human health and agriculture, with associated mortality and morbidity. This project aims to advance artificial intelligence methods to identify mosquito species from high resolution images. While well studied and documented, mosquito species identification remains a highly skilled task, where the few capable of this skill for a given region often have many other job responsibilities, making time devoted to the laborious task of mosquito identification difficult to justify at scale, despite the necessity of the data created. This project and its derivative works will enable organizations without this skill in-house to acquire this highly valuable data. The solution will also allow organizations with this skill in-house to task shift identification to seasonal technicians, and field a larger dataset. This larger dataset would enable better decision making for the control of mosquito borne disease. If successful, these methodologies can be translated to other vectors for disease, further benefiting public health.This Small Business Innovation Research (SBIR) Phase II project is centered around the problem of mosquito species identification. There are more than 3,000 species of mosquitoes in the world, each with different behaviors and capacities for carrying disease. Regionally trained taxonomic experts can identify them through visual inspection, but there is a shortage of such experts. Some artificial intelligence (AI) methods for image-based identification have already been developed, but they are only designed for a limited number of species and face issues due to complex mosquito morphology and the variability incurred in practical use by vector control organizations. This project seeks to enhance existing methodologies for artificial intelligence (AI)-based insect identification by making use of generative models to address issues in training datasets caused by sampling biases. These models will be used to modulate the presence of underrepresented attributes to make a more robust and less biased model. The generative models used for this task will also be used to translate the data for viability in one constrained image domain to another. The final task is to use these models to modulate the training datasets for closely related mosquito species to fine tune performance for minute, but important, distinctions.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.

这项小型企业创新研究（SBIR）第二阶段项目的更广泛影响是使在国内和国际上向公共卫生机构提供高质量的矢量监视数据。将疾病传播给其他生物（如蚊子和壁虱）的载体或生物体对人类健康和农业产生了重大影响，并与相关的死亡率和发病率产生了重大影响。该项目旨在推进人工智能方法，以从高分辨率图像中识别蚊子物种。尽管研究和记录得很好，但蚊子物种的识别仍然是一项高技能的任务，在该任务中，少数能够为特定地区提供此技能的能力通常具有许多其他的工作职责，尽管有必要创建数据，但仍将时间用于浪费的蚊子识别任务很难在大规模上证明是合理的。该项目及其衍生作品将使没有此技能内部的组织能够获取这些高度有价值的数据。该解决方案还将允许内部具有此技能的组织将任务转换为季节性技术人员，并将较大的数据集验证。这个较大的数据集将为控制蚊子疾病的控制提供更好的决策。 如果成功的话，这些方法可以转化为其他疾病媒介，进一步使公共卫生受益。这项小型企业创新研究（SBIR）II期项目围绕蚊子物种鉴定问题。世界上有3,000多种蚊子，每种蚊子具有不同的行为和携带疾病的能力。经过区域训练的分类专家可以通过视觉检查识别它们，但是此类专家短缺。已经开发了一些用于基于图像的识别的人工智能（AI）方法，但由于复杂的蚊子形态以及向量控制组织实际使用而产生的可变性，它们仅专为有限数量的物种和面部问题而设计。该项目旨在通过利用生成模型来解决由采样偏见引起的培训数据集中的问题，以增强现有的人工智能方法（AI）基于昆虫的识别方法。这些模型将用于调节代表性不足的属性的存在，以使更强大且较少的偏见模型。用于此任务的生成模型也将用于将一个约束图像域中的可行性转换为另一个。最终的任务是使用这些模型来调节训练数据集以密切相关的蚊子物种，以微调表现，但重要的是，该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来通过评估来进行评估的。