$$\mathrm {IterMask^2}$$: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRI
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IterMask3D unifies the initial prediction and subsequent mask-shrinking steps into a single model for the entire mask-shrinking process.
“In this work, we unify these steps into a single model for the entire maskshrinking process.”
From IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR (2025) · cites this paper as Liang et al., 2024
Cites
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · see citing works
Self-citationIncidental Findings from 16,400 Brain MRI Examinations of Research Volunteers
Rowley, P. A., Paukner, M. J., Eisenmenger, L. B., Field, A. S., Davidson, R. J., Johnson, S. C., Asthana, S., Chin, N. A., Prabhakaran, V., Bendlin, B. B., et al. (2023) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7821 · Cited by 24 · see citing works
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Liang, Z., Guo, X., Xu, W., Ibrahim, Y., Voets, N., Pretorius, P. M., Noble, J. A., & Kamnitsas, K. (2026) · Medical Image Analysis · doi:10.1016/j.media.2025.103763 · Cited by 3 · see citing works
Self-citationLiang, Z., Guo, X., Xu, W., Ibrahim, Y., Voets, N., Pretorius, P. M., Noble, J. A., & Kamnitsas, K. (2025) · arXiv
Self-citation
- Authors
- Liang, Z., Guo, X., Noble, J. A., & Kamnitsas, K.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Liang, Ziyun
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 339-348
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4403150361
- Cited by
- 10
- References
- 2
- Citations by year
- 2026: 4 · 2025: 6
- FWCI
- 2.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/T028572/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- brain lesion segmentation, magnetic resonance imaging, spatial masking, reconstruction error, false positive reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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