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Objective <p>This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.</p> Methods <p>The Lung Nodule Analysis 2016 (LUNA16) public dataset was used for validation. It was split into training and testing sets in an 8:2 ratio. Classical You Only Look Once (YOLO) models of three sizes (n, m, x) were trained on the training set. Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer (DETR) with Improved DeNoising AnchOr Boxes (DINO), i.e., Grounding DINO and Open-Vocabulary DINO (OV-DINO), as well as our proposed few-shot learning model, across a range of different shot sizes. Finally, all trained models were compared on the test set.</p> Results <p>After training on LUNA16, the precision, recall, and mean average precision (mAP) of the different-sized YOLO models showed no significant differences, with peak values of 82.8%, 73.1%, and 77.4%, respectively. OV-DINO’s recall was significantly higher than YOLO’s, but it did not show clear advantages in precision or mAP. Using only one-fifth of the training samples and one-tenth of the training epochs, our proposed model outperformed both YOLO and OV-DINO, achieving improvements of 6.6%, 9.3%, and 6.9% in precision, recall, and mAP, respectively, with final values of 89.4%, 96.2%, and 87.7%.</p> Conclusion <p>The proposed few-shot learning model demonstrates stronger scene transfer capabilities, requiring fewer samples and training epochs, and can effectively improve the accuracy of lung nodule detection.</p>

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Few-Shot Learning for CT Lung Nodule Detection Based on Open-Set Object Detection

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Lin-meng Li,
  • Huan Zhang,
  • Hai-tao Yu,
  • Bin Cui,
  • Zhi-qun Wang

摘要

Objective

This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.

Methods

The Lung Nodule Analysis 2016 (LUNA16) public dataset was used for validation. It was split into training and testing sets in an 8:2 ratio. Classical You Only Look Once (YOLO) models of three sizes (n, m, x) were trained on the training set. Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer (DETR) with Improved DeNoising AnchOr Boxes (DINO), i.e., Grounding DINO and Open-Vocabulary DINO (OV-DINO), as well as our proposed few-shot learning model, across a range of different shot sizes. Finally, all trained models were compared on the test set.

Results

After training on LUNA16, the precision, recall, and mean average precision (mAP) of the different-sized YOLO models showed no significant differences, with peak values of 82.8%, 73.1%, and 77.4%, respectively. OV-DINO’s recall was significantly higher than YOLO’s, but it did not show clear advantages in precision or mAP. Using only one-fifth of the training samples and one-tenth of the training epochs, our proposed model outperformed both YOLO and OV-DINO, achieving improvements of 6.6%, 9.3%, and 6.9% in precision, recall, and mAP, respectively, with final values of 89.4%, 96.2%, and 87.7%.

Conclusion

The proposed few-shot learning model demonstrates stronger scene transfer capabilities, requiring fewer samples and training epochs, and can effectively improve the accuracy of lung nodule detection.