One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.

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Title: One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.
Authors: Liao H; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China., Yuan J; College of Computer and Information Science, Southwest University, Chongqing, 400715, China., Liu C; Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China., Zhang J; Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China., Yang Y; Department of Pathology, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China., Liang H; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China., Liu H; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China., Chen S; College of Computer and Information Science, Southwest University, Chongqing, 400715, China. csxpml@163.com., Li Y; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China. lymzhang70@163.com.
Source: La Radiologia medica [Radiol Med] 2024 Nov; Vol. 129 (11), pp. 1559-1574. Date of Electronic Publication: 2024 Oct 16.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Springer Milan Country of Publication: Italy NLM ID: 0177625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1826-6983 (Electronic) Linking ISSN: 00338362 NLM ISO Abbreviation: Radiol Med Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.
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  Data: <searchLink fieldCode="AU" term="%22Liao+H%22">Liao H</searchLink>; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.<br /><searchLink fieldCode="AU" term="%22Yuan+J%22">Yuan J</searchLink>; College of Computer and Information Science, Southwest University, Chongqing, 400715, China.<br /><searchLink fieldCode="AU" term="%22Liu+C%22">Liu C</searchLink>; Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Zhang+J%22">Zhang J</searchLink>; Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Yang+Y%22">Yang Y</searchLink>; Department of Pathology, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China.<br /><searchLink fieldCode="AU" term="%22Liang+H%22">Liang H</searchLink>; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.<br /><searchLink fieldCode="AU" term="%22Liu+H%22">Liu H</searchLink>; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.<br /><searchLink fieldCode="AU" term="%22Chen+S%22">Chen S</searchLink>; College of Computer and Information Science, Southwest University, Chongqing, 400715, China. csxpml@163.com.<br /><searchLink fieldCode="AU" term="%22Li+Y%22">Li Y</searchLink>; Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China. lymzhang70@163.com.
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  Data: <searchLink fieldCode="JN" term="%220177625%22">La Radiologia medica</searchLink> [Radiol Med] 2024 Nov; Vol. 129 (11), pp. 1559-1574. <i>Date of Electronic Publication: </i>2024 Oct 16.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer+Milan%22">Springer Milan </searchLink><i>Country of Publication: </i>Italy <i>NLM ID: </i>0177625 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1826-6983 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200338362%22">00338362 </searchLink><i>NLM ISO Abbreviation: </i>Radiol Med <i>Subsets: </i>MEDLINE
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        Value: 10.1007/s11547-024-01902-y
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        Text: English
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      – TitleFull: One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.
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              M: 11
              Text: 2024 Nov
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              Y: 2024
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