{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T07:02:01Z","timestamp":1790146921306,"version":"4.0.1"},"reference-count":31,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Accurate breast cancer classification in dynamic contrast-enhanced (DCE) magnetic resonance imaging requires effective integration of volumetric morphology and temporal contrast-enhancement kinetics. In this study, we evaluate a multi-temporal 3D framework for three-class breast classification (normal, benign, malignant) using the multi-center ODELIA dataset. The framework uses a temporal-as-channel (TaC) input representation that maps three DCE subtraction phases onto the native three-channel input of a pretrained R3D-18 backbone, enabling early cross-phase interaction without a dedicated temporal module. A 3D Spatial attention module (SAM), adapted from the spatial branch of CBAM, is inserted at the first residual stage (SAM-L1) to refine early volumetric features. Experiments identify second-phase subtraction (Sub\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:msub>\n                            <mml:mn\/>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ) as the best-performing single-volume input among those evaluated and show higher observed performance for the 3D full-volume configuration than for the 2D maximum-intensity projection configuration. TaC yields a larger gain in the 3D setting than in the 2D setting, while the attention ablations identify SAM-L1 as the best-performing attention configuration among those tested. The TaC+SAM-L1 configuration achieves an internal-test average score of 81.46%, with a micro-averaged area under the receiver operating characteristic curve of 91.50%, sensitivity at 90% specificity of 76.74%, and specificity at 90% sensitivity of 76.16%.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/aea7a5","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T22:48:42Z","timestamp":1789512522000},"page":"055020","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient multi-temporal 3D learning for breast DCE-MRI classification with temporal-as-channel encoding and early spatial attention"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7463-4982","authenticated-orcid":true,"given":"Thuy","family":"Nguyen Thi","sequence":"first","affiliation":[{"name":"University of Information Technology"},{"name":"Vietnam National University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3849-5015","authenticated-orcid":false,"given":"Hung","family":"Le Minh","sequence":"additional","affiliation":[{"name":"Vietnam Aviation Academy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"266","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"bpexaea7a5bib1","doi-asserted-by":"publisher","first-page":"4474","DOI":"10.1007\/s00330-025-11406-6","type":"journal-article","article-title":"Deep learning-based breast cancer diagnosis in breast mri: systematic review and meta-analysis","volume":"35","author":"Abdullah","year":"2025","journal-title":"Eur. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-06-02","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-15","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-23","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}