Abstract
Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.
Abstract (translated)
乳腺癌是全球女性最常见的死亡原因之一，每年有数百万人因此丧生。磁共振成像（MRI）能够提供多种序列来表征肿瘤形态和内部模式，并已成为检测和诊断乳腺肿瘤的有效工具。然而，基于深度学习的肿瘤分割方法由于癌症区域与正常区域对比度低以及边界模糊的问题，在准确定位肿瘤轮廓方面存在局限性。利用文本提示信息有望通过勾勒出分割区域来改善肿瘤分割效果。受此启发，我们提出了一个由分阶段视觉-语言交互和证据学习引导的文字辅助乳腺肿瘤分割模型（TextBCS）。具体来说，所提出的分阶段视觉-语言交互促进了在每次下采样过程中视觉特征与文本特征之间的信息交流，在低对比度场景中进一步发挥了文本提示定位病灶区域的优势。此外，采用证据学习来量化模糊边界处的分割不确定性。它利用变分狄利克雷来刻画分割概率分布，从而解决了边界的分割不确定性问题。广泛的实验验证了我们的TextBCS模型相较于其他分割网络的优越性，并在公开数据集上展示了最佳的乳腺肿瘤分割性能。
URL
https://arxiv.org/abs/2603.11206
