Baskaran Sundaram
Thomas Jefferson University
74 Papers
287 Citations
Baskaran Sundaram is an academic researcher from Thomas Jefferson University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 25, co-authored 68 publications. Previous affiliations of Baskaran Sundaram include Thomas Jefferson University Hospital & University of Michigan.
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Papers
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.
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
Paras Lakhani,Baskaran Sundaram +1 more
TL;DR: Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99 and an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow.
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Analysis of the Lung Microbiome in the “Healthy” Smoker and in COPD
John R. Erb-Downward,Deborah L. Thompson,MeiLan K. Han,Christine M. Freeman,Christine M. Freeman,Lisa McCloskey,Lisa McCloskey,Lindsay A. Schmidt,Vincent B. Young,Galen B. Toews,Galen B. Toews,Jeffrey L. Curtis,Jeffrey L. Curtis,Baskaran Sundaram,Fernando J. Martinez,Gary B. Huffnagle +15 more
TL;DR: The data suggests the existence of a core pulmonary bacterial microbiome that includes Pseudomonas, Streptococcus, Prevotella, Fusobacterium, Haemophilus, Veillonella, and Porphyromonas within the same lung of subjects with advanced COPD.
Molecular Testing Guideline for the Selection of Patients With Lung Cancer for Treatment With Targeted Tyrosine Kinase Inhibitors: American Society of Clinical Oncology Endorsement of the College of American Pathologists/International Association for the Study of Lung Cancer/Association for Molecular Pathology Clinical Practice Guideline Update.
Gregory P. Kalemkerian,Navneet Narula,Erin B. Kennedy,William A. Biermann,Jessica S. Donington,Natasha B. Leighl,Madelyn Lew,James Pantelas,Suresh S. Ramalingam,Martin Reck,Anjali Saqi,Michael Simoff,Navneet Singh,Navneet Singh,Baskaran Sundaram +14 more
TL;DR: This update clarifies that any sample with adequate cellularity and preservation may be tested and that analytical methods must be able to detect mutation in a sample with as little as 20% cancer cells and strongly recommends against evaluating epidermal growth factor receptor expression by immunohistochemistry for selection of patients for EGFR-targeted therapy.
Radiologic–pathologic discordance in biopsy-proven usual interstitial pneumonia
Kunihiro Yagihashi,Jason Huckleberry,Thomas V. Colby,Henry D. Tazelaar,Jordan A. Zach,Baskaran Sundaram,Sudhakar Pipavath,Marvin I. Schwarz,David A. Lynch +8 more
TL;DR: In this population of patients enrolled with a diagnosis of idiopathic pulmonary fibrosis, 94.7% of those with HRCT findings “inconsistent with UIP” demonstrated histological UIP, suggesting that the term’s misleading.
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