Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis
重新思考乳腺X线摄影转移学习:用于乳腺癌筛查和病变诊断的数据集知情转移学习(DITL)框架
机构 * Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg(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) ; Siemens Healthineers(西门子医疗)
AI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。
Comments 16 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