PFI-TT: Trustworthy Artificial Intelligence for the Volumetric Evaluation of Brain Tumors

这一创新-技术转化伙伴关系(PFI-TT)项目的更广泛影响/商业潜力包括一项创新技术，用于开发值得信赖的人工智能(AI)模型，并将其应用于脑癌监测和治疗。如果成功，这项技术将使治疗脑癌的医生能够在脑部磁共振成像(MRI)扫描中快速识别和标记肿瘤的边界。准确和可靠地标记脑肿瘤的边界是至关重要的，因为许多原因，包括计划未来的治疗，量化当前治疗的效果，或监测癌症肿瘤随时间的进展。该团队正在开发的值得信赖的人工智能模型预计将适用于其他医学成像适应症。此外，这些模型可以很容易地扩展到其他需要准确、高度可靠和强大的技术解决方案的工程领域。鉴于目前人工智能模型应用的增长，人工智能模型的可信性和可靠性的挑战将显著增加。根据该奖项正在开发的技术可能会解决这些挑战，并通过医疗、智能交通系统和制造等领域的社会应用对数百万人的生活产生积极影响。拟议的项目将开发一种值得信赖的基于人工智能的脑肿瘤分割解决方案，以识别和标记脑部MRI扫描中的肿瘤边界。该模型被称为编解码网中具有不确定性传播的医学图像分割模型(Super-Net)。超级网络的一个显著特点是同时输出像素级的肿瘤分割图和相应的像素级不确定性图。分割图在MRI中识别肿瘤的边界，而不确定性图传达了模型对分割的信心。这是通过在模型训练期间传播数据和模型参数中的置信度或不确定性来实现的。该项目将专注于开发和验证用于在脑磁共振中分割肿瘤的超级网络架构。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/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project consists of an innovative technology for developing trustworthy artificial intelligence (AI) models with applications in brain cancer monitoring and treatment. If successful, this technology will enable doctors who treat brain cancers to quickly identify and mark the boundaries of tumors in brain magnetic resonance imaging (MRI) scans. The accurate and reliable marking of boundaries of brain tumors is essential for many reasons, including planning future treatments, quantifying the effect of current treatment, or monitoring the progression of the cancerous tumor over time. The trustworthy AI models that the team is developing are expected to be applicable to other medical imaging indications. In addition, these models could be easily extended to other engineering areas that require accurate, highly reliable, and robust technological solutions. Given the current growth in the applications of AI models, the challenges of the trustworthiness and reliability of AI models will increase significantly. The technology being developed under this award may address these challenges and positively impact the lives of millions of people through societal applications in areas such as medicine, intelligent transportation systems, and manufacturing. The proposed project will develop a trustworthy AI-based brain tumor segmentation solution to identify and mark boundaries of tumors in brain MRI scans. The model is called the medical image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks (SUPER-Net). A defining characteristic of SUPER-Net is that it simultaneously outputs a pixel-level tumor segmentation map and a corresponding pixel-level uncertainty map. The segmentation map identifies the tumor’s boundaries in an MRI, and the uncertainty map conveys the model’s confidence in its segmentations. This is achieved by propagating belief or uncertainty in the data and the model’s parameters during the model’s training. The project will focus on developing and validating SUPER-Net architecture for segmenting tumors in brain MRIs.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.