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Due to variations in acquisition conditions, medical image data often exhibit substantial domain shift, which decreases the performance of detection models. Prior works suffer from suboptimal performance and time inefficiencies in cross-domain detection. Specifically, we designed a domain adaptation module to further process the features output by the prototype to complete the detection. Furthermore, we have restructured the backbone architecture through FasterNet. Using the proposed domain discriminator and gradient reversal layer, we conducted domain adversarial training for the module. Finally, we tested the proposed DA-YOLO model on brain tumor datasets collected under various conditions. The experimental results demonstrate that the proposed FDA-YOLO is well performed in domain adaptation, particularly on unseen datasets, outperforming YOLOv11 and its general variants. Compared to the baseline model, FDA-YOLO achieves notable improvements in scenarios with substantial cross-domain discrepancies, including a 9.5% increase in precision and a 4.44% boost in mAP@50, all without sacrificing computational efficiency.

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FDA-YOLO: Fast Domain Adaptation YOLO for Cross-Domain Brain Tumor Detection in Medical Imaging

  • 会议论文

  • 发表日期：

    2025年

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  • Kai Yang,
  • Junyong He,
  • Fugao Li,
  • Ting Li

摘要

Due to variations in acquisition conditions, medical image data often exhibit substantial domain shift, which decreases the performance of detection models. Prior works suffer from suboptimal performance and time inefficiencies in cross-domain detection. Specifically, we designed a domain adaptation module to further process the features output by the prototype to complete the detection. Furthermore, we have restructured the backbone architecture through FasterNet. Using the proposed domain discriminator and gradient reversal layer, we conducted domain adversarial training for the module. Finally, we tested the proposed DA-YOLO model on brain tumor datasets collected under various conditions. The experimental results demonstrate that the proposed FDA-YOLO is well performed in domain adaptation, particularly on unseen datasets, outperforming YOLOv11 and its general variants. Compared to the baseline model, FDA-YOLO achieves notable improvements in scenarios with substantial cross-domain discrepancies, including a 9.5% increase in precision and a 4.44% boost in mAP@50, all without sacrificing computational efficiency.