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Citations Report

Citation Index - Imaging in Medicine [149 Articles]

The articles published in Imaging in Medicine have been cited 149 times by eminent researchers all around the world. Following is the list of articles that have cited the articles published in Imaging in Medicine.

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  • Kwak JT, Hewitt SM, Sinha S, et al. Multimodal microscopy for automated histologic analysis of prostate cancer. BMC cancer. 11,62 (2011). View at Publisher | View at Google Scholar
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  • Di Cataldo S, Ficarra E, Macii E. Computer-aided techniques for chromogenic immunohistochemistry: status and directions. Computers in biology and medicine. 42,1012-1025 (2012).

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  • Cruz-Roa A, Basavanhally A, González F, Gilmore H, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. InSPIE medical imaging 2014 Mar 20 (pp. 904103-904103). International Society for Optics and Photonics. View at Publisher | View at Google Scholar
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  • Chappelow J, Tomaszewski JE, Feldman M, et al. HistoStitcher©: An interactive program for accurate and rapid reconstruction of digitized whole histological sections from tissue fragments. Computerized Medical Imaging and Graphics. 35,557-567 (2011). View at Publisher | View at Google Scholar
  • Cruz-Roa A, Díaz G, Romero E, et al. Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization. Journal of pathology informatics. 2 (2011). View at Publisher | View at Google Scholar
  • Sparks R, Madabhushi A. Statistical shape model for manifold regularization: Gleason grading of prostate histology. Computer Vision and Image Understanding. 117,1138-1146 (2013). View at Publisher | View at Google Scholar
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  • Xu J, Sparks R, Janowczyk A, et al. High-throughput prostate cancer gland detection, segmentation, and classification from digitized needle core biopsies. InInternational Workshop on Prostate Cancer Imaging.Springer Berlin Heidelberg 77-88 (2010). View at Publisher | View at Google Scholar
  • Lewis Jr JS, Ali S, Luo J, et al. A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. The American journal of surgical pathology. 38,128 (2014). View at Publisher | View at Google Scholar
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  • Ali S, Veltri R, Epstein JA, Christudass C, Madabhushi A. Cell cluster graph for prediction of biochemical recurrence in prostate cancer patients from tissue microarrays. InSPIE Medical Imaging. International Society for Optics and Photonics. 86760H-86760H (2013). View at Publisher | View at Google Scholar
  • Ali S, Veltri R, Epstein JI, Christudass C, Madabhushi A. Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays. Computerized medical imaging and graphics. 41,3-13 (2015).

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  • Arevalo J, Cruz-Roa A, Arias V, et al. An unsupervised feature learning framework for basal cell carcinoma image analysis. Artificial intelligence in medicine. 64,131-145 (2015). View at Publisher | View at Google Scholar
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  • Xu Y, Jiao L, Wang S, Wei J, Fan Y, Lai M, Chang EI. Multi‐label classification for colon cancer using histopathological images. Microscopy research and technique. 76,1266-77 (2013). View at Publisher | View at Google Scholar
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  • Sanchez V, Aulí-Llinàs F, Vanam R, et al. Rate control for lossless region of interest coding in HEVC intra-coding with applications to digital pathology images. InAcoustics, Speech and Signal Processing (ICASSP), IEEE International Conference. 1250-1254 (2015). View at Publisher | View at Google Scholar
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  • Doyle S, Madabhushi A. Consensus of ambiguity: theory and application of active learning for biomedical image analysis. InIAPR International Conference on Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg. 313-324 (2010). View at Publisher | View at Google Scholar
  • Fatima K, Arooj A, Majeed H. A new texture and shape based technique for improving meningioma classification. Microscopy research and technique. 77,862-873 (2014). View at Publisher | View at Google Scholar
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  • Cruz-Roa A, Arevalo J, Basavanhally A, Madabhushi A, González F. A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation. InTenth International Symposium on Medical Information Processing and Analysis 2015 Jan 28 (pp. 92870G-92870G). International Society for Optics and Photonics. View at Publisher | View at Google Scholar
  • Bhargava R, Madabhushi A. Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology. Annual Review of Biomedical Engineering. 18,387-412 (2016). View at Publisher | View at Google Scholar
  • Hipp JD, Smith SC, Sica J, Lucas D, Hipp JA, Kunju LP, Balis UJ. Tryggo: Old norse for truth-The real truth about ground truth: New insights into the challenges of generating ground truth maps for WSI CAD algorithm evaluation. Journal of pathology informatics. 3,8 (2012). View at Publisher | View at Google Scholar
  • Ravindran U, Shakila T. Content based image retrieval for histology image collection using visual pattern mining. International Journal of Scientific & Engineering Research. 4 (2013). View at Publisher | View at Google Scholar
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