In medical image analysis, misclassifications such as false negatives can have grave implications, emphasizing the critical need to mitigate noise within datasets that contribute to these errors. While deep neural networks thrive on voluminous data, the prohibitive cost of curating noise-free datasets in medical image analysis poses challenges in assembling high-quality, large-scale data.Particularly in object detection, the landscape of potential noise is multifaceted, encompassing not only categorization noise but also issues like localization noise, missing annotations, and bogus bounding boxes. Despite this complexity, much of the existing literature has been limited in scope, often addressing only specific types of noise, such as localization or categorization.In response, this paper introduces the Universal-Noise Annotation (UNA), a holistic framework designed to capture the full spectrum of noise types inherent in object detection. We investigate the influence of UNA on detector performance, explore the evolution of past detection algorithms, and pinpoint factors that enhance the robustness of detection model training approaches.For the broader research community's benefit, we have open-sourced our code for integrating UNA into datasets, and we also provide complete access to our training logs and weights.