Computer Science Faculty Publications and Presentations
Document Type
Conference Proceeding
Publication Date
4-2025
Abstract
Deep learning methods have demonstrated strong performance in object detection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare whether large, general-purpose datasets with diverse objects outperform niche datasets with characteristics similar to polyps. In addition, we assess the influence of the size of the dataset on the efficacy of transfer learning. Experiments on the polyp datasets show that models pre-trained on relevant datasets consistently outperform those trained from scratch, highlighting the benefit of pre-training on datasets with shared domain-specific features.
Recommended Citation
Fabian Vazquez, Jose Angel Nuñez, Xiaoyan Fu, Pengfei Gu, and Bin Fu "Exploring transfer learning for deep learning polyp detection in colonoscopy images using YOLOv8", Proc. SPIE 13407, Medical Imaging 2025: Computer-Aided Diagnosis, 134071V (4 April 2025); https://doi.org/10.1117/12.3049019
Publication Title
Proceedings Volume 13407, Medical Imaging 2025: Computer-Aided Diagnosis
DOI
10.1117/12.3049019
Comments
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.