Summary of Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model, by Sangjoon Park et al.
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model
by Sangjoon Park, Yong Bae Kim, Jee Suk Chang, Seo Hee Choi, Hyungjin Chung, Ik Jae Lee, Hwa Kyung Byun
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As advancements in breast cancer treatment continue to progress, assessing post-surgical cosmetic outcomes has gained significance due to its substantial impact on patients’ quality of life. To address the limitations of conventional supervised learning and existing anomaly detection models, we present a novel automated approach: Attention-Guided Denoising Diffusion Anomaly Detection (AG-DDAD). This approach leverages the attention mechanism of the 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. Our method provides visually appealing representations and quantifiable scores for cosmesis evaluation, surpassing existing models in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are working to improve how breast cancer surgery affects a patient’s appearance. They want to make sure that patients have the best possible outcomes after their treatment. The researchers developed a new way to look at pictures of breasts and rate how good they look. This method is special because it can look at pictures without needing any labels or help from people. The scientists tested this method with real pictures and found that it works very well. This study helps improve how we evaluate the results of breast surgery. |
Keywords
» Artificial intelligence » Anomaly detection » Attention » Diffusion » Diffusion model » Self supervised » Supervised » Unsupervised » Vision transformer » Vit
