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

这一小型企业创新研究(SBIR)第二阶段项目的更广泛影响是能够向国内和国际公共卫生机构提供高质量的病媒监测数据。媒介生物或将疾病传播给其他生物的生物，如蚊子和扁虱，对人类健康和农业有重大影响，并伴随着相关的死亡率和发病率。该项目旨在推进人工智能方法，从高分辨率图像中识别蚊子物种。尽管有充分的研究和记录，但蚊子物种鉴定仍然是一项高度熟练的任务，在特定地区，少数能够掌握这项技能的人往往还有许多其他工作职责，这使得投入到蚊子鉴定这一艰巨任务上的时间很难大规模证明是合理的，尽管需要创建数据。该项目及其衍生作品将使内部没有这项技能的组织能够获得这些非常有价值的数据。该解决方案还将允许拥有此技能的组织在内部将识别任务转移到季节性技术人员，并现场部署更大的数据集。这个更大的数据集将使控制蚊媒疾病的决策更好。如果成功，这些方法可以转化为其他传播疾病的媒介，进一步造福公众健康。这项小型企业创新研究(SBIR)第二阶段项目围绕蚊子物种识别问题展开。世界上有3000多种蚊子，每种蚊子都有不同的行为和携带疾病的能力。经过地区培训的分类学专家可以通过肉眼检查来识别它们，但这样的专家缺乏。已经开发了一些基于图像的人工智能(AI)识别方法，但由于蚊子复杂的形态和媒介控制组织在实际使用中遇到的变异性，这些方法仅适用于有限的物种和面部问题。该项目旨在加强现有的基于人工智能(AI)的昆虫识别方法，利用产生式模型来解决由于采样偏差导致的训练数据集中的问题。这些模型将被用来调整未被充分代表的属性的存在，以形成更稳健和更少偏见的模型。用于这项任务的生成模型也将被用于将一个受限图像域中的生存能力数据转换到另一个图像域中。最后的任务是使用这些模型来调整密切相关蚊子物种的训练数据集，以微调表现出微小但重要的区别。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 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.