Purpose To train and assess the performance of a deep learning-based network designed to detect lesions in the liver parenchyma on abdominal US images, localize focal liver lesions (FLLs), and characterize them. Materials and Methods In this retrospective, multicenter, institutional review board-approved study, two object detectors, Faster Recurrent Convolutional Neural Network (Faster-RCNN) and DEtection vision TRansformer (DETR) were fine-tuned on a dataset of 1026 patients (n = 2551 B-mode abdominal US images between 2014 and 2018). Performances were analyzed on a test set of 48 new patients (n = 155 B-mode abdominal US images between 2018 and 2019) and were compared with that of three caregivers, one nonexpert and two experts, blinded to clinical history. A sign test was used to statistically compare accuracy, specificity, sensitivity and PPV between all raters. Results DETR had a specificity of 90% (95% CI: 75, 100) and a sensitivity of 97% (95% CI: 97, 97) for the detection of FLLs. Performances met or exceeded that of the three caregivers for this task. It correctly localized 80% of the lesions, and had a specificity of 81% (95% CI: 67, 91) and a sensitivity of 82% (95% CI: 62, 100) for FLL characterization (benign versus malignant) among lesions localized by all raters. Performances met or exceeded that of two experts and Faster-RCNN. Conclusion DETR demonstrated high specificity for detection, localization, and characterization of FLLs on abdominal US images.