KD-SqueezeNet: an efficient deep learning strategy for the multi-task diagnosis of neonatal lung diseases.
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| Title: | KD-SqueezeNet: an efficient deep learning strategy for the multi-task diagnosis of neonatal lung diseases. |
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| Authors: | Li J; The Department of Radiology, Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Xueyuan West Road, Wenzhou, 325027, China.; The Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China., Pan R; Hangzhou Normal University, Hangzhou, China., Tian F; Sichuan Agricultural University, Yucheng, China., Pan P; The Department of Radiology, Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Xueyuan West Road, Wenzhou, 325027, China.; Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China., Yan Z; The Department of Radiology, Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Xueyuan West Road, Wenzhou, 325027, China. yanzhihanwz@163.com.; Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China. yanzhihanwz@163.com. |
| Source: | Pediatric radiology [Pediatr Radiol] 2026 Feb 20. Date of Electronic Publication: 2026 Feb 20. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 0365332 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1998 (Electronic) Linking ISSN: 03010449 NLM ISO Abbreviation: Pediatr Radiol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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