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Natural images depict real-world scenes such as landscapes, animals, and everyday items. Transformer-based detectors, such as the Detection Transformer, have demonstrated strong object detection performance on natural image datasets. These models are typically optimized through complex engineering strategies tailored to the characteristics of natural scenes. However, medical imaging presents unique challenges, such as high resolutions, smaller and fewer regions of interest, and subtle inter-class differences, which differ significantly from natural images. In this study, we evaluated the effectiveness of common design choices in transformer-based detectors when applied to medical imaging. Using two representative datasets, a mammography dataset and a chest CT dataset, we showed that common design choices proposed for natural images, including complex encoder architectures, multi-scale feature fusion, query initialization, and iterative bounding box refinement, fail to improve and can even be detrimental to the object detection performance. In contrast, simpler and shallower architectures often achieve equal or superior results with less computational cost. These findings highlight that standard design practices need to be reconsidered when adapting transformer models to medical imaging, and suggest that simplicity may be more effective than added complexity in this domain. Our model code and weights are publicly available at https://github.com/nyukat/Mammo-DETR