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Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 (MICCAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15006))

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Abstract

Medical anomaly detection (AD) is crucial in pathological identification and localization. Current methods typically rely on uncertainty estimation in deep ensembles to detect anomalies, assuming that ensemble learners should agree on normal samples while exhibiting disagreement on unseen anomalies in the output space. However, these methods may suffer from inadequate disagreement on anomalies or diminished agreement on normal samples. To tackle these issues, we propose D2UE, a Diversified Dual-space Uncertainty Estimation framework for medical anomaly detection. To effectively balance agreement and disagreement for anomaly detection, we propose Redundancy-Aware Repulsion (RAR), which uses a similarity kernel that remains invariant to both isotropic scaling and orthogonal transformations, explicitly promoting diversity in learners’ feature space. Moreover, to accentuate anomalous regions, we develop Dual-Space Uncertainty (DSU), which utilizes the ensemble’s uncertainty in input and output spaces. In input space, we first calculate gradients of reconstruction error with respect to input images. The gradients are then integrated with reconstruction outputs to estimate uncertainty for inputs, enabling effective anomaly discrimination even when output space disagreement is minimal. We conduct a comprehensive evaluation of five medical benchmarks with different backbones. Experimental results demonstrate the superiority of our method to state-of-the-art methods and the effectiveness of each component in our framework. Our code is available at https://github.com/Rubiscol/D2UE.

Y. Gu and Y. Lin—Equal contribution.

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Notes

  1. 1.

    Ablation studies of \(\lambda \) and the layer of \(\mathcal {L}_{\text {sim}}\) are included in supplementary materials.

  2. 2.

    https://www.kaggle.com/c/rsna-pneumonia-detection-challenge.

  3. 3.

    https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection.

  4. 4.

    https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset.

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Acknowledgement

This work was supported by the Hong Kong Innovation and Technology Fund (Project No. MHP/002/22), Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone (HZQB-KCZYB-2020083) and the Research Grants Council of the Hong Kong (Project Reference Number: T45-401/22-N).

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Gu, Y., Lin, Y., Cheng, KT., Chen, H. (2024). Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15006. Springer, Cham. https://doi.org/10.1007/978-3-031-72089-5_49

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  • DOI: https://doi.org/10.1007/978-3-031-72089-5_49

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