Two-stage deep learning framework for laterally spreading tumors detection using self-supervised learning and few-shot classification

Background Laterally spreading tumors (LSTs) of the colorectum are flat lesions with precancerous potential that are often overlooked during routine colonoscopy due to their subtle morphology and low prevalence, posing a significant challenge for early detection. Aim To address the scarcity of labeled LSTs data and the limitations of conventional supervised learning, we formulate LSTs recognition as an image-level binary classification problem (LSTs vs non-LSTs) using colonoscopy images. The aim of this study is to achieve reliable discrimination under limited expert annotations. Methods A large-scale dataset of 150,168 colonoscopy images was retrospectively collected from 12,376 patients at Shanghai General Hospital between July 2021 and July 2025. The framework included two components: (1) DINO self-supervised pretraining on 150,168 unlabeled images to learn robust visual representations, and (2) Prototypical Networks performing few-shot classification with 2799 labeled training images. The model was evaluated on 601 test images using comprehensive metrics including area under the receiver operating characteristic curve (ROC-AUC), sensitivity, and specificity. Results The proposed model demonstrated robust performance, achieving an overall accuracy of 72.4% (95% confidence interval (CI): 67.2–77.7%), sensitivity of 74.7%, specificity of 70.2%, and ROC-AUC of 0.798 (95% CI: 0.746–0.850). Notably, the model also attained an F1-score of 0.730 and a precision–recall AUC of 0.828. The optimal decision threshold determined by Youden's index was 0.338. With a rapid processing speed of 50 ms per image, the framework is well-suited for real-time clinical applications. Conclusions By combining self-supervised representation learning with few-shot classification, our framework reduces the need for large annotated datasets while maintaining clinically relevant performance. This approach offers a practical pathway toward AI-assisted LSTs detection in resource-constrained endoscopic settings and lays the groundwork for future integration into real-time colonoscopy support systems.