  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.

Computer Vision and Pattern Recognition;Artificial Intelligence