发表机构
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg; Siemens Healthineers（模式识别实验室，埃尔朗根-纽伦堡弗里德里希-亚历山大大学; 西门子医疗）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对乳腺X线摄影分类性能提升难题，提出DITL框架，整合数据集难度信号与邻域监督，引入自适应组件，无需超参数调整，在大规模和小数据集上均有出色表现，建立了通用的乳腺X线摄影分类框架。
AI 中文摘要
在小型精选数据集和大规模临床队列中，提高乳腺X线摄影的分类性能一直是个挑战。传统转移学习方法常忽略数据集特定特征，近期邻域知情方法受限于狭窄任务和固定公式，扩展性不足。为此提出数据集知情转移学习（DITL）框架，将数据集衍生的难度信号与基于邻域的三元组监督整合在统一目标中。DITL引入两个自适应组件：自适应难度加权交叉熵（A-DWCE）和自适应邻域表示三元组（A-NR-Triplet）。在大规模VinDR-Mammo数据集上，DITL在全图像乳腺密度分类中取得了领先性能，在小ROI数据集上也有显著提升。DITL建立了一个临床相关、可扩展且通用的乳腺X线摄影分类框架。
英文摘要
Enhancing classification performance in mammography remains a persistent challenge across both small curated datasets and large-scale clinical cohorts. Conventional transfer learning approaches often neglect dataset-specific characteristics, while recent neighborhood-informed methods have been restricted to narrow tasks with rigid formulations, limiting their scalability to population-level datasets. To address these challenges, we propose the Dataset-Informed Transfer Learning (DITL) framework, which integrates dataset-derived difficulty signals with neighborhood-based triplet supervision in a unified objective. DITL introduces two adaptive components: (i) Adaptive Difficulty-Weighted Cross-Entropy (A-DWCE), which assigns per-sample weights based on k-nearest neighbor label purity in a self-supervised feature space, and (ii) Adaptive Neighborhood Representation Triplet (A-NR-Triplet), which enforces intra-class compactness and inter-class separation using a learnable margin. Unlike focal loss, DITL requires no hyperparameter tuning, removes heuristic weighting and fixed margins, and incurs negligible computational overhead, yielding a robust and scalable optimization strategy. On the large-scale VinDR-Mammo dataset, DITL achieves state-of-the-art performance for whole-image breast density classification, with significant improvements across accuracy, F1-score, and AUC (p < 0.0001). Beyond large cohorts, DITL also delivers consistent, statistically significant gains on small ROI datasets (p < 0.0001). By bridging small-scale lesion analysis with large-scale density estimation, DITL establishes a clinically relevant, scalable, and generalizable framework for mammography classification, spanning the full breast cancer screening-to-diagnosis spectrum.
Comments16 pages, 1 figure, 5 tables. Accepted and presented at the 10th International Conference on Computer Vision & Image Processing (CVIP 2025), IIT Ropar, India, 10-13 December 2025. The paper is currently in press for inclusion in the official conference proceedings. This preprint corresponds to the submitted manuscript and is made available pending publication of the final proceedings version
