Label-independent framework for objective evaluation of cosmetic outcome in breast cancer

Sangjoon Park; Yong Bae Kim; Jee Suk Chang; Seo Hee Choi; Hyungjin Chung; Ik Jae Lee; Hwa Kyung Byun

doi:With advancements in the field of breast cancer treatment, the assessment of postsurgical cosmetic outcomes has gained increasing significance owing to its substantial impact on patients' quality of life. However, evaluating breast cosmesis is challenging because of 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. The model addresses the limitations of conventional supervised learning and existing anomaly detection models. Our approach leverages the attention mechanism of distillation with no labels and a self-supervised vision transformer, combined 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 adopted an unsupervised anomaly detection perspective to automatically score the cosmesis. Real-world data experiments demonstrated the effectiveness of our method, providing visually appealing representations and quantifiable scores for cosmesis evaluation. Compared with commonly used rule-based programs, our fully automated approach eliminates the need for manual annotations and offers an objective evaluation. Moreover, our anomaly detection model exhibits state-of-the-art performance, surpassing existing models in terms of accuracy. 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.

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Label-independent framework for objective evaluation of cosmetic outcome in breast cancer

DC Field	Value	Language
dc.contributor.author	김용배	-
dc.contributor.author	박상준	-
dc.contributor.author	변화경	-
dc.contributor.author	이익재	-
dc.contributor.author	장지석	-
dc.contributor.author	최서희	-
dc.date.accessioned	2025-10-15T01:34:02Z	-
dc.date.available	2025-10-15T01:34:02Z	-
dc.date.issued	2025-09	-
dc.identifier.issn	0933-3657	-
dc.identifier.uri	https://ir.ymlib.yonsei.ac.kr/handle/22282913/207410	-
dc.description.abstract	Anomaly detection; Breast cosmesis; Diffusion model; Vision transformer	-
dc.description.statementOfResponsibility	https://www.sciencedirect.com/science/article/pii/S0933365725001149	-
dc.language	English	-
dc.publisher	Elsevier Science Publishing	-
dc.relation.isPartOf	ARTIFICIAL INTELLIGENCE IN MEDICINE	-
dc.rights	CC BY-NC-ND 2.0 KR	-
dc.subject.MESH	1	-
dc.title	Label-independent framework for objective evaluation of cosmetic outcome in breast cancer	-
dc.type	Article	-
dc.contributor.college	College of Medicine (의과대학)	-
dc.contributor.department	Dept. of Radiation Oncology (방사선종양학교실)	-
dc.contributor.googleauthor	Sangjoon Park	-
dc.contributor.googleauthor	Yong Bae Kim	-
dc.contributor.googleauthor	Jee Suk Chang	-
dc.contributor.googleauthor	Seo Hee Choi	-
dc.contributor.googleauthor	Hyungjin Chung	-
dc.contributor.googleauthor	Ik Jae Lee	-
dc.contributor.googleauthor	Hwa Kyung Byun	-
dc.identifier.doi	With advancements in the field of breast cancer treatment, the assessment of postsurgical cosmetic outcomes has gained increasing significance owing to its substantial impact on patients' quality of life. However, evaluating breast cosmesis is challenging because of 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. The model addresses the limitations of conventional supervised learning and existing anomaly detection models. Our approach leverages the attention mechanism of distillation with no labels and a self-supervised vision transformer, combined 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 adopted an unsupervised anomaly detection perspective to automatically score the cosmesis. Real-world data experiments demonstrated the effectiveness of our method, providing visually appealing representations and quantifiable scores for cosmesis evaluation. Compared with commonly used rule-based programs, our fully automated approach eliminates the need for manual annotations and offers an objective evaluation. Moreover, our anomaly detection model exhibits state-of-the-art performance, surpassing existing models in terms of accuracy. 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.	-
dc.contributor.localId	A00744	-
dc.contributor.localId	A06513	-
dc.contributor.localId	A05136	-
dc.contributor.localId	A03055	-
dc.contributor.localId	A04658	-
dc.contributor.localId	A04867	-
dc.relation.journalcode	J04230	-
dc.identifier.eissn	1873-2860	-
dc.identifier.pmid	40505180	-
dc.subject.keyword	Breast Neoplasms* / diagnostic imaging	-
dc.subject.keyword	Breast Neoplasms* / surgery	-
dc.subject.keyword	Female	-
dc.subject.keyword	Humans	-
dc.subject.keyword	Image Processing, Computer-Assisted* / methods	-
dc.subject.keyword	Quality of Life	-
dc.contributor.alternativeName	Kim, Yong Bae	-
dc.contributor.affiliatedAuthor	김용배	-
dc.contributor.affiliatedAuthor	박상준	-
dc.contributor.affiliatedAuthor	변화경	-
dc.contributor.affiliatedAuthor	이익재	-
dc.contributor.affiliatedAuthor	장지석	-
dc.contributor.affiliatedAuthor	최서희	-
dc.citation.volume	167	-
dc.citation.startPage	103179	-
dc.identifier.bibliographicCitation	ARTIFICIAL INTELLIGENCE IN MEDICINE, Vol.167 : 103179, 2025-09	-
dc.identifier.articleno	10.1016/j.artmed.2025.103179	-

Appears in Collections:: 1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers

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