Transformer-based detectors have shown success in computer vision tasks with natural images. These models, exemplified by the Deformable DETR, are optimized through complex engineering strategies tailored to the typical characteristics of natural scenes. However, medical imaging data presents unique challenges such as extremely large image sizes, fewer and smaller regions of interest, and object classes which can be differentiated only through subtle differences. This study evaluates the applicability of these transformer-based design choices when applied to a screening mammography dataset that represents these distinct medical imaging data characteristics. Our analysis reveals that common design choices from the natural image domain, such as complex encoder architectures, multi-scale feature fusion, query initialization, and iterative bounding box refinement, do not improve and sometimes even impair object detection performance in medical imaging. In contrast, simpler and shallower architectures often achieve equal or superior results. This finding suggests that the adaptation of transformer models for medical imaging data requires a reevaluation of standard practices, potentially leading to more efficient and specialized frameworks for medical diagnosis.

医学影像数据中的对象检测性能通常不会因为常见的自然图像领域的设计选择而改善，甚至有时会降低，因此适应医学影像数据的变压器模型需要重新评估标准做法，从而可能导致更有效和专门用于医学诊断的框架。

理解应用DETR于自然图像与医学图像的差异