解决肠息肉分类中标签分布偏移问题，提升模型泛化能力。
Polyp-D2ATL: Deep Domain-Adaptive Transfer Learning for Colorectal Polyp Classification under Label Distribution Shift
- 基于深度域自适应迁移学习，应对标签分布变化。
- 在PICCOLO数据集上达82.38%准确率，宏F1为77.49%。
- 适合临床场景下复杂息肉的自动分类任务。
早期且高精度地预测结直肠息肉作为最危险癌症的重要标志，可挽救更多生命。尽管息肉分类技术已取得进展，但在真实场景中仍面临诸多挑战：模型需处理不平衡数据、标签分布偏移以及跨模态泛化问题，尤其对特征多样的难分类息肉表现不佳。本文提出Polyp-D2ATL框架及专用训练策略，有效缓解上述限制，实现对NICE分类体系下各类息肉的精准预测。在PICCOLO验证集与测试集上的大量实验表明，该方法显著优于现有SOTA模型，在验证集上达到82.38%准确率、77.49%宏F1和87.47%特异性，测试集上亦保持一致提升，证明其强泛化能力与临床应用潜力。
原文摘要 · Abstract (English)
Early and highly accurate prediction of colorectal polyps, as an important sign of one of the most dangerous types of cancer, will result in saving more lives. Despite the advancements in colorectal polyp classification, many challenges remain in obtaining an automated polyp prediction system that is able to diagnose the difficult-to-predict polyps accompanied by different features in real scenarios, where the model can handle imbalanced data, label distribution shift, and cross-modality generalization successfully. In this study, we propose Polyp-D2ATL, a novel framework accompanied by a specific training strategy, which mitigates these limitations and effectively predicts the different classes of polyps belonging to the NICE classification. Our extensive experiments on the PICCOLO validation and test sets demonstrate that the proposed Polyp-D2ATL significantly outperforms existing state-of-the-art models across various reliable metrics, achieving an accuracy of 82.38%, a Macro-F1 of 77.49%, and a specificity of 87.47% on the validation set, alongside consistent improvements on the held-out test set which demonstrates the generalization capacity and clinical applicability of the proposed approach.
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