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Detecting phenotypic anomalies in microscopy images, especially those caused by genetic mutations or environmental factors, is an important task in biomedical image analysis. We propose a novel unsupervised anomaly detection method, Self-Attention Parts Guidance (SAPAG), which leverages latent diffusion models trained on normal images. SAPAG operates in two steps: (1) it segments self-attention maps into semantically distinct regions using an unsupervised segmentation model (DiffSeg), and (2) it performs region-wise reconstruction during the reverse diffusion process guided by self-attention guidance (SAG). We validated SAPAG with microscopy images of Caenorhabditis elegans embryos. Wild-type embryo images were used as normal data, and RNAi-treated embryo images, which may contain phenotypic anomalies, served as test data. SAPAG outperformed representative anomaly detection methods, including PatchCore, RD4AD, and THOR, especially in detecting anomalies in embryo shape and size. Ablation studies further confirmed that both SAG and DiffSeg contribute to detection performance. Although subtle small structural anomalies (e.g. cell nuclear-level changes) remain challenging, SAPAG demonstrates strong potential for high-throughput phenotypic screening in microscopy-based analysis.

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SAPAG: Self-attention Parts Guidance with Latent Diffusion Models for Anomaly Detection in Microscopy Images

  • 会议论文

  • 发表日期：

    2026年

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  • Kazuki Shimizu,
  • Yukako Tohsato

摘要

Detecting phenotypic anomalies in microscopy images, especially those caused by genetic mutations or environmental factors, is an important task in biomedical image analysis. We propose a novel unsupervised anomaly detection method, Self-Attention Parts Guidance (SAPAG), which leverages latent diffusion models trained on normal images. SAPAG operates in two steps: (1) it segments self-attention maps into semantically distinct regions using an unsupervised segmentation model (DiffSeg), and (2) it performs region-wise reconstruction during the reverse diffusion process guided by self-attention guidance (SAG). We validated SAPAG with microscopy images of Caenorhabditis elegans embryos. Wild-type embryo images were used as normal data, and RNAi-treated embryo images, which may contain phenotypic anomalies, served as test data. SAPAG outperformed representative anomaly detection methods, including PatchCore, RD4AD, and THOR, especially in detecting anomalies in embryo shape and size. Ablation studies further confirmed that both SAG and DiffSeg contribute to detection performance. Although subtle small structural anomalies (e.g. cell nuclear-level changes) remain challenging, SAPAG demonstrates strong potential for high-throughput phenotypic screening in microscopy-based analysis.