Objective and Interpretable Breast Cosmesis Evaluation with Attention
  Guided Denoising Diffusion Anomaly Detection Model
Sangjoon Park Yong Bae Kim Jee Suk Chang Seo Hee Choi Hyungjin Chung Ik Jae Lee Hwa Kyung Byun
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.