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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Published on: April 21, 2023

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

Lin-Meng Li¹, Huan Zhang¹, Hai-Tao Yu¹

¹Department of Radiology, Aerospace Center Hospital, Beijing, 100049, China.

Current Medical Science

|November 13, 2025

Summary

A novel few-shot learning model significantly improves lung nodule detection accuracy in CT scans. This model requires fewer training samples and epochs than existing methods, demonstrating superior performance in precision, recall, and mAP.

Keywords:

CT imaging Few-shot learning Lung nodule Open-set object detection Vision query

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Lung nodule detection is crucial for early lung cancer diagnosis.
Current deep learning models often require large annotated datasets, limiting their application.

Purpose of the Study:

To develop a few-shot learning model for lung nodule detection in CT images.
To leverage visual open-set object detection techniques for improved efficiency.

Main Methods:

Utilized the Lung Nodule Analysis 2016 (LUNA16) dataset for training and testing.
Compared classical You Only Look Once (YOLO) models with advanced open-set models like Grounding DINO and Open-Vocabulary DINO (OV-DINO).
Developed and evaluated a novel few-shot learning model against established benchmarks.

Main Results:

YOLO models achieved peak precision, recall, and mAP of 82.8%, 73.1%, and 77.4%, respectively.
OV-DINO showed higher recall than YOLO but no significant advantage in precision or mAP.
The proposed few-shot model outperformed YOLO and OV-DINO, achieving 89.4% precision, 96.2% recall, and 87.7% mAP with reduced training data and epochs.

Conclusions:

The developed few-shot learning model exhibits enhanced scene transfer capabilities.
It requires significantly fewer training samples and epochs for effective lung nodule detection.
The model demonstrates a substantial improvement in the accuracy of lung nodule detection in CT images.