用临床描述生成乳腺超声肿瘤图像，可精准控制形态与特征。
Breast Ultrasound Tumor Generation via Mask Generator and Text-Guided Network:A Clinically Controllable Framework with Downstream Evaluation
- 结合临床描述与结构掩码生成肿瘤，实现形态等特征的精细控制。
- 在6个公开数据集上验证，显著提升下游诊断任务性能。
- 超声科医生评测确认生成图像真实，适合临床应用扩展。
乳腺超声（BUS）图像分析的深度学习模型发展受限于专家标注数据稀缺。为此，我们提出一种临床可控的生成框架，用于合成BUS图像。该框架融合临床描述与结构掩码，生成具有特定形态、回声强度和形状特征的肿瘤。设计了语义-曲率掩码生成器，基于临床先验生成结构多样的肿瘤掩码。推理时，合成掩码输入生成框架，产出高度个性化的含瘤超声图像，反映真实世界中的形态多样性。在六个公开BUS数据集上的定量评估显示，合成图像显著提升下游乳腺癌诊断任务表现。此外，由经验丰富的超声医师进行的视觉图灵测试证实生成图像具备高真实性，表明该框架在更广泛临床应用中具有潜力。
原文摘要 · Abstract (English)
The development of robust deep learning models for breast ultrasound (BUS) image analysis is significantly constrained by the scarcity of expert-annotated data. To address this limitation, we propose a clinically controllable generative framework for synthesizing BUS images. This framework integrates clinical descriptions with structural masks to generate tumors, enabling fine-grained control over tumor characteristics such as morphology, echogencity, and shape. Furthermore, we design a semantic-curvature mask generator, which synthesizes structurally diverse tumor masks guided by clinical priors. During inference, synthetic tumor masks serve as input to the generative framework, producing highly personalized synthetic BUS images with tumors that reflect real-world morphological diversity. Quantitative evaluations on six public BUS datasets demonstrate the significant clinical utility of our synthetic images, showing their effectiveness in enhancing downstream breast cancer diagnosis tasks. Furthermore, visual Turing tests conducted by experienced sonographers confirm the realism of the generated images, indicating the framework's potential to support broader clinical applications.
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