Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?
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| Title: | Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way? |
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| Authors: | Baselli G; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Golgi 39, 20133, Milan, Italy., Codari M; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Golgi 39, 20133, Milan, Italy. marina.codari@polimi.it.; Present Address: Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr., Stanford, CA, 94305, USA. marina.codari@polimi.it., Sardanelli F; Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097, Italy.; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, San Donato Milanese, 20097, Italy. |
| Source: | European radiology experimental [Eur Radiol Exp] 2020 May 05; Vol. 4 (1), pp. 30. Date of Electronic Publication: 2020 May 05. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: SpringerOpen Country of Publication: England NLM ID: 101721752 Publication Model: Electronic Cited Medium: Internet ISSN: 2509-9280 (Electronic) Linking ISSN: 25099280 NLM ISO Abbreviation: Eur Radiol Exp Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2509-9280 |
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| DOI: | 10.1186/s41747-020-00159-0 |
