As advancements in the field of breast cancer treatment continue to progress, the assessment of post-surgical cosmetic outcomes has gained increasing significance due to its substantial impact on patients' quality of life. However, evaluating breast cosmesis presents challenges due to the inherently subjective nature of expert labeling. In this study, we present a novel automated approach, Attention-Guided Denoising Diffusion Anomaly Detection (AG-DDAD), designed to assess breast cosmesis following surgery, addressing the limitations of conventional supervised learning and existing anomaly detection models. Our approach leverages the attention mechanism of the distillation with no label (DINO) self-supervised Vision Transformer (ViT) in combination with a diffusion model to achieve high-quality image reconstruction and precise transformation of discriminative regions. By training the diffusion model on unlabeled data predominantly with normal cosmesis, we adopt an unsupervised anomaly detection perspective to automatically score the cosmesis. Real-world data experiments demonstrate the effectiveness of our method, providing visually appealing representations and quantifiable scores for cosmesis evaluation. Compared to commonly used rule-based programs, our fully automated approach eliminates the need for manual annotations and offers objective evaluation. Moreover, our anomaly detection model exhibits state-of-the-art performance, surpassing existing models in accuracy. Going beyond the scope of breast cosmesis, our research represents a significant advancement in unsupervised anomaly detection within the medical domain, thereby paving the way for future investigations.

翻译：随着乳腺癌治疗领域的不断进步，术后美容效果的评估因其对患者生活质量的显著影响而日益重要。然而，由于专家标注固有的主观性，乳腺癌美容效果评估面临挑战。在本研究中，我们提出了一种新颖的自动化方法——注意力引导的扩散去噪异常检测（Attention-Guided Denoising Diffusion Anomaly Detection, AG-DDAD），旨在评估术后乳腺癌美容效果，以弥补传统监督学习及现有异常检测模型的局限性。该方法结合了无标签蒸馏（DINO）自监督视觉变换器（Vision Transformer, ViT）的注意力机制与扩散模型，实现高质量的图像重建及区分性区域的精确变换。通过主要针对正常美容效果的无标签数据训练扩散模型，我们采用无监督异常检测视角自动评分美容效果。真实世界数据实验证明了该方法的有效性，提供了视觉上具有吸引力的表征及可量化的美容评估分数。与常用的基于规则的流程相比，我们的全自动化方法无需手动标注，并提供客观评估。此外，该异常检测模型展现了最先进的性能，在准确性上超越现有模型。超越乳腺癌美容效果范畴，本研究代表了医学领域无监督异常检测的重大进展，从而为未来研究铺平道路。