Abstract
The error is caused by special characters that arXiv's system doesn't recognize. Here's the cleaned version with all problematic characters replaced: Breast magnetic resonance imaging is a critical tool for cancer detection and treatment planning, but its clinical utility is hindered by poor specificity, leading to high false-positive rates and unnecessary biopsies. This study introduces a transformer-based framework for automated classification of breast lesions in dynamic contrast-enhanced MRI, addressing the challenge of distinguishing benign from malignant findings. We implemented a SegFormer architecture that achieved an AUC of 0.92 for lesion-level classification, with 100% sensitivity and 67% specificity at the patient level - potentially eliminating one-third of unnecessary biopsies without missing malignancies. The model quantifies malignant pixel distribution via semantic segmentation, producing interpretable spatial predictions that support clinical decision-making. To establish reproducible benchmarks, we curated BreastDCEDL_AMBL by transforming The Cancer Imaging Archive's AMBL collection into a standardized deep learning dataset with 88 patients and 133 annotated lesions (89 benign, 44 malignant). This resource addresses a key infrastructure gap, as existing public datasets lack benign lesion annotations, limiting benign-malignant classification research. Training incorporated an expanded cohort of over 1,200 patients through integration with BreastDCEDL datasets, validating transfer learning approaches despite primary tumor-only annotations. Public release of the dataset, models, and evaluation protocols provides the first standardized benchmark for DCE-MRI lesion classification, enabling methodological advancement toward clinical deployment.
Abstract (translated)
错误是由arXiv系统无法识别的特殊字符引起的。以下是替换所有问题字符后的清理版本： 乳房磁共振成像是癌症检测和治疗计划中的关键工具，但其临床效用受到较低特异性的限制，导致假阳性率高并引发不必要的活检。本研究介绍了一种基于变压器框架的方法，用于动态对比增强MRI中乳腺病变的自动分类，解决了区分良性与恶性发现这一挑战。我们实施了SegFormer架构，在病灶级别的分类上达到了0.92的AUC值，并且在患者级别上实现了100%的灵敏度和67%的特异性——有可能减少三分之一不必要的活检而不遗漏恶性肿瘤。该模型通过语义分割量化恶性的像素分布，生成支持临床决策制定的可解释的空间预测结果。 为了建立可重复的标准基准，我们整理了BreastDCEDL_AMBL数据集，将癌症影像档案馆的AMBL集合转换为包含88名患者和133个标记病灶（其中89个良性，44个恶性）的标准深度学习数据集。这一资源弥补了一个关键基础设施缺口，因为现有的公共数据集中缺少良性病变标注信息，限制了良恶性分类的研究。 训练阶段整合了超过1,200名患者的扩增队列，并通过与BreastDCEDL数据集的集成验证了迁移学习方法的有效性，尽管主要肿瘤注释仅限于单一肿瘤。公开发布该数据集、模型以及评估协议为DCE-MRI病灶分类提供了首个标准基准，促进了向临床应用的方法改进。
URL
https://arxiv.org/abs/2509.26440
