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About this paper
Yang Nan, Huichi Zhou, Xiaodan Xing, Giorgos Papanastasiou, Lei Zhu, Zhifan Gao, Alejandro F Fangi, Guang Yang
An Anomaly-aware Content-based Image Recommendation (ACIR) framework is introduced to enhance medical image retrieval by consolidating knowledge from hierarchical features and optimizing functions. The framework demonstrates a 5.6-38.9% improvement in mean Average Precision (mAP) over existing methods and achieves 90.2% precision in out-of-distribution (OOD) sample detection on the RadIN-CT dataset.
Key insights
- Consolidating hierarchical features from both ConvNets and Vision Transformers via depth-aware fusion enhances comprehensive image representation.
- Addressing over-centralization in contrastive hashing by integrating image structural fingerprints improves the distinction of visually diverse samples within the same category.
- A self-supervised reconstruction approach effectively identifies out-of-distribution medical images, improving system robustness.
- Augmenting hash-based retrieval with content similarity-based ranking provides a more nuanced and accurate fine-grained differentiation of results.
Summary by alphaXiv.
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