385 lines
43 KiB
HTML
385 lines
43 KiB
HTML
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<title>NLM Intramural Research Program - Sameer Antani, PhD</title>
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<meta name="DC.Date.Issued" content="2019-10-29" />
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<h1 class="forstaffpages" style="opacity: 1;">Sameer Antani, PhD</h1>
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<h3>Sameer Antani, PhD</h3>
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Tenure Track Investigator, Computational Health Research Branch
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<p>Research in machine learning and artificial intelligence, medical image and signal processing, and information retrieval for solving challenging problems and advancing health.</p>
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<h3>Contact Information</h3>
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<p>Building 38A, 8600 Rockville Pike MSC 6075 Bethesda, MD 20894</p>
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<p>Tel: (301) 435-3218</p>
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<p><a href="mailto:sameer.antani@nih.gov">sameer.antani@nih.gov</a></p>
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<h2>Research Interests</h2>
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<p><iframe src="https://www.youtube.com/embed/cOwSU_3yjFw" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" width="709" height="399" frameborder="0"></iframe><p>
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<p>Dr. Sameer Antani is a Principal Investigator (Tenure-Track) in the Division of Intramural Research of the National Library of Medicine (NLM) at the National Institutes of Health (NIH). He earned his Ph.D. and M.Eng. in Computer Science and Engineering from the Pennsylvania State University and his B.E. (First Class with Distinction) in Computer Engineering from the Savitribai Phule Pune University (formerly University of Pune), India. Dr. Antani is a Fellow of the American Institute for Medical and Biological Engineering (<a href="https://aimbe.org/">AIMBE</a>), Fellow of Institute of Electrical and Electronics Engineers (<a href="https://www.ieee.org/">IEEE</a>), and Senior Member of the <a href="https://spie.org/about-spie">SPIE</a>. His research focuses on novel algorithms in medical imaging and multimodal machine learning (ML) and artificial intelligence (AI), ML/AI for resource-limited and global health settings, and characterizing data and innovations in ML/AI algorithm design for reliable AI predictions with applications in disease screening, diagnostics, risk prediction, and treatment. His scientific contributions and research leadership have been recognized through several awards including NLM Board of Regents Awards and NIH Director's Awards. </p>
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<h2>Publications</h2>
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<span class="authors">
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Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani S.
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</span><a href="https://www.ncbi.nlm.nih.gov/pubmed/38608333/">
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Semantically redundant training data removal and deep model classification performance: A study with chest X-rays.
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</a><span class="source">Comput Med Imaging Graph</span>.
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<span class="pubdate">2024 Jul;</span><span class="volume">115</span><span class="pages">:102379</span><span>. </span><span class="doi">doi: 10.1016/j.compmedimag.2024.102379. </span><span class="pubstatus">Epub 2024 Apr 9. </span><span class="pmid">PubMed PMID:
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38608333</span><span class="pmcid">; PubMed Central PMCID:
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PMC11144082</span><span>. </span>
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<div class="ncbi-docsum">
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<span class="authors">
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Spirnak JR, Antani S.
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</span><a href="https://www.ncbi.nlm.nih.gov/pubmed/37864817/">
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The Need for Artificial Intelligence Curriculum in Military Medical Education.
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</a><span class="source">Mil Med</span>.
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<span class="pubdate">2024 May 18;</span><span class="volume">189</span><span class="issue">(5-6)</span><span class="pages">:954-958</span><span>. </span><span class="doi">doi: 10.1093/milmed/usad412. </span><span class="pmid">PubMed PMID:
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37864817</span><span class="pmcid">; PubMed Central PMCID:
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PMC11439989</span><span>. </span>
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<span class="authors">
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Xue Z, Oguguo T, Yu KJ, Chen TC, Hua CH, Kang CJ, Chien CY, Tsai MH, Wang CP, Chaturvedi AK, Antani S.
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</span><a href="https://www.ncbi.nlm.nih.gov/pubmed/38774479/">
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Cleaning and Harmonizing Medical Image Data for Reliable AI: Lessons Learned from Longitudinal Oral Cancer Natural History Study Data.
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</a><span class="source">Proc SPIE Int Soc Opt Eng</span>.
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<span class="pubdate">2024 Feb;</span><span class="volume">12931</span><span>. </span><span class="doi">doi: 10.1117/12.3005875. </span><span class="pubstatus">Epub 2024 Apr 2. </span><span class="pmid">PubMed PMID:
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38774479</span><span class="pmcid">; PubMed Central PMCID:
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PMC11107840</span><span>. </span>
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<span class="authors">
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Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani S.
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</span><a href="https://www.ncbi.nlm.nih.gov/pubmed/38232121/">
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Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images.
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</a><span class="source">PLOS Digit Health</span>.
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<span class="pubdate">2024 Jan;</span><span class="volume">3</span><span class="issue">(1)</span><span class="pages">:e0000286</span><span>. </span><span class="doi">doi: 10.1371/journal.pdig.0000286. </span><span class="pubstatus">eCollection 2024 Jan. </span><span class="pmid">PubMed PMID:
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38232121</span><span class="pmcid">; PubMed Central PMCID:
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PMC10793885</span><span>. </span>
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<h3><a href="https://www.ncbi.nlm.nih.gov/myncbi/sameer.antani.1/bibliography/public/">More</a></h3>
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