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BMAD: Benchmarks for Medical Anomaly Detection

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arxiv 2306.11876 v3 pith:LQK642QD submitted 2023-06-20 eess.IV cs.CV

Jinan Bao , Hanshi Sun , Hanqiu Deng , Yinsheng He , Zhaoxiang Zhang , Xingyu Li This is my paper · ORCID
classification eess.IVcs.CV
keywords medicalanomalybenchmarkdetectionmethodsbmadcomprehensiveevaluation
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Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images. This benchmark encompasses six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fourteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables comprehensive comparisons among recently proposed anomaly detection methods. It will facilitate the community to conduct a fair comparison and advance the field of AD on medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMAD

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Simulation to Real Scans: Anomaly Detection in Maritime Cargo with Muon Scattering Tomography

    physics.ins-det 2026-08 conditional novelty 6.0 of 10

    An unsupervised detector trained only on simulated benign cargo scans finds contraband in real muon-scattering scans of shipping containers when scored with the Homogeneity Index.

  2. MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence

    cs.CV 2026-03 reject novelty 5.0 of 10

    MEDIC-AD adds anomaly-aware and difference tokens to a medical VLM, claiming SOTA lesion detection, temporal tracking, and visual grounding; the zero-shot claim is undermined by likely train/test overlap.

  3. SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    SP-Mamba pairs a Mamba autoencoder with sliding-window anatomical prototypes and concentration/contrast anomaly scoring, reporting state-of-the-art unsupervised medical anomaly detection on three benchmarks.

  4. Towards Continual Visual Anomaly Detection in the Medical Domain

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    A continual-learning variant of PatchCore matches task-specific models on medical image anomaly detection with less than 1% forgetting.

  5. MoViAD: A Modular Library for Visual Anomaly Detection

    cs.CV 2025-07 reject novelty 3.0 of 10

    A modular visual anomaly detection library is described, but without code, benchmarks, or experimental validation of its capabilities.

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