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Cited by in F6Publishing
For: Leng X, Uddin KMS, Chapman W Jr, Luo H, Kou S, Amidi E, Yang G, Chatterjee D, Shetty A, Hunt S, Mutch M, Zhu Q. Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning. Radiology 2021;299:349-58. [PMID: 33754826 DOI: 10.1148/radiol.2021202208] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Klibanov AL. High-Precision Assessment of Chemoradiotherapy of Rectal Cancer with Near-Infrared Photoacoustic Microscopy and Deep Learning. Radiology 2021;299:359-61. [PMID: 33759582 DOI: 10.1148/radiol.2021210261] [Reference Citation Analysis]
2 Rajendran P, Sharma A, Pramanik M. Photoacoustic imaging aided with deep learning: a review. Biomed Eng Lett . [DOI: 10.1007/s13534-021-00210-y] [Reference Citation Analysis]
3 Leng X, Amidi E, Kou S, Cheema H, Otegbeye E, Chapman WJ, Mutch M, Zhu Q. Rectal Cancer Treatment Management: Deep-Learning Neural Network Based on Photoacoustic Microscopy Image Outperforms Histogram-Feature-Based Classification. Front Oncol 2021;11:715332. [PMID: 34631543 DOI: 10.3389/fonc.2021.715332] [Reference Citation Analysis]
4 Wang Z, Yang F, Cheng Z, Zhang W, Xiong K, Yang S. Photoacoustic-guided photothermal therapy by mapping of tumor microvasculature and nanoparticle. Nanophotonics 2021;10:3359-68. [DOI: 10.1515/nanoph-2021-0198] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]