{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:08:14Z","timestamp":1774868894506,"version":"3.50.1"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"JSPS KAKENHI","award":["24H00720"],"award-info":[{"award-number":["24H00720"]}]},{"name":"Cabinet Office Cross-ministerial innovation Program"},{"name":"CIBoG WISE program"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Purpose:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>In this paper, we propose a novel generative model to produce high-quality SAH samples, enhancing SAH CT detection performance in imbalanced datasets. Previous methods, such as cost-sensitive learning and previous diffusion models, suffer from overfitting or noise-induced distortion, limiting their effectiveness. Accurate SAH sample generation is crucial for better detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Methods:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>\n                      We propose the Worley\u2013Perlin Diffusion Model (WPDM), leveraging Worley\u2013Perlin noise to synthesize diverse, high-quality SAH images. WPDM addresses limitations of Gaussian noise (homogeneity) and Simplex noise (distortion), enhancing robustness for generating SAH images. Additionally,\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$\\hbox {WPDM}_{\\text {Fast}}$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:msub>\n                              <mml:mtext>WPDM<\/mml:mtext>\n                              <mml:mtext>Fast<\/mml:mtext>\n                            <\/mml:msub>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      optimizes generation speed without compromising quality.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Results:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>WPDM effectively improved classification accuracy in datasets with varying imbalance ratios. Notably, a classifier trained with WPDM-generated samples achieved an F1-score of 0.857 on a 1:36 imbalance ratio, surpassing the state of the art by 2.3 percentage points.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Conclusion:<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>WPDM overcomes the limitations of Gaussian and Simplex noise-based models, generating high-quality, realistic SAH images. It significantly enhances classification performance in imbalanced settings, providing a robust solution for SAH CT detection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-025-03482-2","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T12:48:00Z","timestamp":1756817280000},"page":"457-471","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic data generation with Worley\u2013Perlin diffusion for robust subarachnoid hemorrhage detection in imbalanced CT Datasets"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1870-1014","authenticated-orcid":false,"given":"Zhongyang","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9324-9230","authenticated-orcid":false,"given":"Tao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-422X","authenticated-orcid":false,"given":"Masahiro","family":"Oda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9402-8079","authenticated-orcid":false,"given":"Yutaro","family":"Fuse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2484-1360","authenticated-orcid":false,"given":"Ryuta","family":"Saito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8241-6565","authenticated-orcid":false,"given":"Masahiro","family":"Jinzaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0100-4797","authenticated-orcid":false,"given":"Kensaku","family":"Mori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"issue":"1","key":"3482_CR1","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1038\/s41467-024-46015-2","volume":"15","author":"S Thilak","year":"2024","unstructured":"Thilak S, Brown P, Whitehouse T, Gautam N, Lawrence E, Ahmed Z, Veenith T (2024) Diagnosis and management of subarachnoid haemorrhage. 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