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1 Ergebnisse
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Evaluating the Segment Anything Model for Histopathological..:
Hieber, D
;
Karthan, M
;
Holl, F
...
Deng R, Cui C, Liu Q, Yao T, Remedios LW, Bao S, et al. Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging [Preprint]. arXiv. 2023 Apr 9. arXiv:2304.04155. DOI:10.48550/arXiv.2304.04155. , 2023
Link:
https://doi.org/10.3205/23gmds099
RT Journal T1 <Title>Evaluating the Segment Anything Model for Histopathological Tissue Segmentation</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftgms:oai:oai-gms.dimdi.de:GM23gmds099&Exemplar=1&LAN=DE A1 Hieber, D A1 Karthan, M A1 Holl, F A1 Prokop, G A1 Märkl, B A1 Pryss, R A1 Liesche-Starnecker, F A1 Schobel, J PB German Medical Science GMS Publishing House; Düsseldorf YR 2023 K1 computer vision K1 machine learning K1 glioblastoma K1 tumor segmentation K1 ddc: 610 JF Deng R, Cui C, Liu Q, Yao T, Remedios LW, Bao S, et al. Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging [Preprint]. arXiv. 2023 Apr 9. arXiv:2304.04155. DOI:10.48550/arXiv.2304.04155 LK http://dx.doi.org/https://doi.org/10.3205/23gmds099 DO https://doi.org/10.3205/23gmds099 SF ELIB - SuUB Bremen
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