| Previous ID Next ID Search for '266164995' |
5001: Papers(Old)
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| DOI | 10.1016/j.radonc.2026.111387 |
| category | 2 |
| imaging | 0.7351678609848022 |
| therapy | 0.7984182834625244 |
| modeling | 0.574340283870697 |
| wos_citations | 0 |
| scopus_citations | 0 |
| COBISS | 266164995 |
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5002: Papers
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| DOI | 10.1016/j.radonc.2026.111387 |
| category | 1 |
| imaging | 0.6948225498199463 |
| therapy | 0.6666202545166016 |
| modeling | 0.6816209554672241 |
| year | 2026 |
| citedness | 0.5994471501908648 |
| wos_citations | 0 |
| scopus_citations | 0 |
| COBISS | 266164995 |
| group | |
| classification | 1.01 |
| title | Dosimetric assessment of deep learning based organ-at-risk segmentation: insights from the HaN-Seg challenge |
| ISSN | 1879-0887 |
| authors | 23404;33446;14576;54815;12531 |
| exclude | |
| firstAuthor | 54815 |
| MR | false |
| MRcoAuthor | false |
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