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

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Abstract

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

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