Detection Transformers represent end-to-end object detection approaches based on a Transformer encoder-decoder architecture, exploiting the attention mechanism for global relation modeling. Although Detection Transformers deliver results on par with or even superior to their highly optimized CNN-based counterparts operating on 2D natural images, their success is closely coupled to access to a vast amount of training data. This, however, restricts the feasibility of employing Detection Transformers in the medical domain, as access to annotated data is typically limited. To tackle this issue and facilitate the advent of medical Detection Transformers, we propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder. Focused Decoder leverages information from an anatomical region atlas to simultaneously deploy query anchors and restrict the cross-attention's field of view to regions of interest, which allows for a precise focus on relevant anatomical structures. We evaluate our proposed approach on two publicly available CT datasets and demonstrate that Focused Decoder not only provides strong detection results and thus alleviates the need for a vast amount of annotated data but also exhibits exceptional and highly intuitive explainability of results via attention weights. Our code is available at https://github.com/bwittmann/transoar.

翻译：检测Transformer是一种基于Transformer编码器-解码器架构的端到端目标检测方法，利用注意力机制进行全局关系建模。尽管检测Transformer在二维自然图像上的性能与高度优化的基于CNN的方法相当甚至更优，但其成功离不开大量训练数据的支撑。然而，在医学领域，由于标注数据通常有限，这限制了检测Transformer的可行性。为解决该问题并推动医学检测Transformer的发展，我们提出了一种用于三维解剖结构检测的新型检测Transformer——Focused Decoder。Focused Decoder利用解剖区域图谱的信息，同时部署查询锚点并将交叉注意力的视野限制在感兴趣区域，从而能够精确聚焦于相关解剖结构。我们在两个公开CT数据集上评估了所提方法，结果表明Focused Decoder不仅提供了强大的检测结果，从而缓解了对大量标注数据的需求，而且通过注意力权重展现了卓越且高度直观的结果可解释性。我们的代码可在https://github.com/bwittmann/transoar获取。