Organ-DETR: Organ Detection via Transformers.

Query-based Transformers have been yielding impressive performance in object localization and detection tasks. However, their application to organ detection in 3D medical imaging data has been relatively unexplored. This study introduces Organ-DETR, featuring two innovative modules, MultiScale Attention (MSA) and Dense Query Matching (DQM), designed to enhance the performance of Detection Transformers (DETRs) for 3D organ detection. MSA is a novel top-down representation learning approach for efficiently encoding Computed Tomography (CT) features. This architecture employs a multiscale attention mechanism, utilizing both dual self-attention and cross-scale attention mechanisms to extract intra- and inter-scale spatial interactions in the attention mechanism. Organ-DETR also introduces DQM, an approach for one-to-many matching that tackles the label assignment difficulties in organ detection. DQM increases positive queries to enhance both recall scores and training efficiency without the need for additional learnable parameters. Extensive results on five 3D CT datasets indicate that the proposed Organ-DETR outperforms comparable techniques by achieving a remarkable improvement of +10.6 mAP COCO. The project and code are available at https://github.com/ai-med/ OrganDETR.      «

Query-based Transformers have been yielding impressive performance in object localization and detection tasks. However, their application to organ detection in 3D medical imaging data has been relatively unexplored. This study introduces Organ-DETR, featuring two innovative modules, MultiScale Attention (MSA) and Dense Query Matching (DQM), designed to enhance the performance of Detection Transformers (DETRs) for 3D organ detection. MSA is a novel top-down representation learning approach for ef...     »