用少量正常图像实现跨器官的异常检测新方法
Dual Distillation for Few-Shot Anomaly Detection
- 双蒸馏机制：教师网络指导学生解码器，支持图自蒸馏
- 仅需少量正常样本，在13,084张医学图像上达最优性能
- 适合医疗影像中少样本异常检测，可推广至多种器官和模态
异常检测在计算机视觉中至关重要，尤其在医学影像中早期发现病灶可直接影响患者预后。现有无监督方法通常依赖大量正常数据，且难以跨解剖结构泛化。本文提出D²⁴FAD，一种用于少样本异常检测的新型双蒸馏框架，仅需少量正常参考图像即可在未见任务中识别异常。该方法利用预训练编码器作为教师网络，从支持图和查询图中提取多尺度特征；学生解码器在查询图上从教师蒸馏知识，并在支持图上进行自蒸馏。进一步提出动态加权机制，根据查询图内容评估每张支持图的参考价值，优化检测效果。为评估方法，我们构建了一个包含13,084张图像的综合性基准数据集，覆盖四个器官、四种成像模态和五类疾病。大量实验表明，D²⁴FAD显著优于现有方法，建立少样本医学异常检测新基准。代码已公开于https://github.com/ttttqz/D24FAD。
原文摘要 · Abstract (English)
Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. While recent unsupervised anomaly detection approaches show promise, they require substantial normal training data and struggle to generalize across anatomical contexts. We introduce D$^2$4FAD, a novel dual distillation framework for few-shot anomaly detection that identifies anomalies in previously unseen tasks using only a small number of normal reference images. Our approach leverages a pre-trained encoder as a teacher network to extract multi-scale features from both support and query images, while a student decoder learns to distill knowledge from the teacher on query images and self-distill on support images. We further propose a learn-to-weight mechanism that dynamically assesses the reference value of each support image conditioned on the query, optimizing anomaly detection performance. To evaluate our method, we curate a comprehensive benchmark dataset comprising 13,084 images across four organs, four imaging modalities, and five disease categories. Extensive experiments demonstrate that D$^2$4FAD significantly outperforms existing approaches, establishing a new state-of-the-art in few-shot medical anomaly detection. Code is available at https://github.com/ttttqz/D24FAD.
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