D
Daniel Max
Researcher at Icahn School of Medicine at Mount Sinai
Publications - 2
Citations - 2007
Daniel Max is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: MEDLINE. The author has an hindex of 2, co-authored 2 publications receiving 1412 citations.
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Journal ArticleDOI
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.
Samuel G. Armato,Geoffrey McLennan,Luc Bidaut,Michael F. McNitt-Gray,Charles R. Meyer,Anthony P. Reeves,Binsheng Zhao,Denise R. Aberle,Claudia I. Henschke,Eric A. Hoffman,Ella A. Kazerooni,Heber MacMahon,Edwin J. R. van Beek,David F. Yankelevitz,Alberto Biancardi,Peyton H. Bland,Matthew S. Brown,Roger Engelmann,Gary E. Laderach,Daniel Max,Richard C. Pais,David Qing,Rachael Y. Roberts,Amanda R. Smith,Adam Starkey,Poonam Batra,Philip Caligiuri,Ali Farooqi,Gregory W. Gladish,C. Matilda Jude,Reginald F. Munden,Iva Petkovska,Leslie E. Quint,Lawrence H. Schwartz,Baskaran Sundaram,Lori E. Dodd,Charles Fenimore,David Gur,Nicholas Petrick,John Freymann,Justin Kirby,Brian Hughes,Alessi Vande Casteele,Sangeeta Gupte,Maha Sallam,Michael D. Heath,Michael Kuhn,Ekta Dharaiya,Richard Burns,David Fryd,Marcos Salganicoff,Vikram Anand,Uri Shreter,Stephen Vastagh,Barbara Y. Croft,Laurence P. Clarke +55 more
TL;DR: The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus and is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
Journal ArticleDOI
Evaluation of Lung MDCT Nodule Annotation Across Radiologists and Methods
Charles R. Meyer,Timothy D. Johnson,Geoffrey McLennan,Denise R. Aberle,Ella A. Kazerooni,Heber MacMahon,Brian F. Mullan,David F. Yankelevitz,Edwin J. R. van Beek,Samuel G. Armato,Michael F. McNitt-Gray,Anthony P. Reeves,David Gur,Claudia I. Henschke,Eric A. Hoffman,Peyton H. Bland,Gary E. Laderach,Richie C. Pais,David Qing,Chris Piker,Junfeng Guo,Adam Starkey,Daniel Max,Barbara Y. Croft,Laurence P. Clarke +24 more
TL;DR: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used, and the random noise component is larger for the p-map analysis than for volume estimation, which appears to have more power to detect differences in radiologist-method combinations.