PROJECT SUMMARY Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are difficult to detect tumors which commonly present at advanced stages, with the liver as the most common site of metastases. 68Ga and 64Cu DOTATATE positron emission tomography-computed tomography (PET/CT) are the most sensitive methods to identify somatostatin receptor subtype 2 positive GEP-NETs, and targeted peptide radionuclide receptor therapy with 177Lu DOTATATE is the most effective systemic therapy for many patients. Despite the clear advantage in progression-free survival compared to prior standard of care, the vast majority of patients (99%) do not have complete response and require additional therapies. Further development of treatments requires an accurate assessment of the response to therapy. However, there is currently an unmet medical need for automated, standardized quantification of 68Ga DOTATATE positive disease burden, which could have a great impact on novel therapeutic drug regimen development. Deep learning-based approaches have recently been applied to automated lesion detection and quantification, and have achieved state-of-the-art performance. These methods, however, do not consider dataset/domain shifts between training and testing data. In dataset/domain shifts, data used to build and train models might have a significantly different distribution from that used for model testing. Therefore, models without considering domain shifts would not generalize well to unseen data, leading to poor lesion detection performance. In this proposed research, we will develop a novel deep learning-based imaging informatics system, termed Geles, for automated, Generalizable lesion detection for livers in GEP-NET PET/CT imaging. This system will use list-mode data acquisition to produce a large, diverse annotated training dataset, followed by novel adversarial learning to enhance model generalizability. The proposed Geles system will consist of two modules, domain generalization and domain adaptation. Aim 1 will develop an adversarial domain generalization module that is generalizable to unseen domains or resources. This module will build a deep neural network with domain-adversarial learning and extract domain-invariant feature representations for individual lesion identification, so that the system can generalize to unseen domain data, such as PET images from different institutions, devices, imaging protocols, and other variations. Aim 2 will develop a target-oriented domain adaptation module that is automatically adaptable to new specific datasets of interest (i.e., target datasets). Given a small set of unannotated images from a certain target dataset, this module will conduct low-resource unsupervised domain adaptation to further boost the lesion detection performance. Specifically, it will build a novel, augmented generative adversarial network for image-to-image translation in a low-resource setting, so that Geles can take advantage of limited, unannotated ...