Citations Report

Imaging in Medicine : Citations & Metrics Report

Articles published in Imaging in Medicine have been cited by esteemed scholars and scientists all around the world.

Imaging in Medicine has got h-index 25, which means every article in Imaging in Medicine has got 25 average citations.

Following are the list of articles that have cited the articles published in Imaging in Medicine.

  2022 2021 2020 2019 2018

Year wise published articles

34 46 30 15 37

Year wise citations received

452 564 500 482 432
Journal total citations count 4878
Journal impact factor 12.71
Journal 5 years impact factor 14.99
Journal cite score 15.98
Journal h-index 25
Journal h-index since 2018 20
Journal Impact Factor 2020 formula
IF= Citations(y)/{Publications(y-1)+ Publications(y-2)} Y= Year
Journal 5-year Impact Factor 2020 formula
Citations(2016 + 2017 + 2018 + 2019 + 2020)/
{Published articles(2016 + 2017 + 2018 + 2019 + 2020)}
Journal citescore
Citescorey = Citationsy + Citationsy-1 + Citationsy-2 + Citations y-3 / Published articlesy + Published articlesy-1 + Published articlesy-2 + Published articles y-3
  • Xiong H, Liu X, Tian X, Pu L, Zhang H, Lu M, Huang W, Zhang YT. A numerical study of the effect of varied blood pressure on the stability of carotid atherosclerotic plaque. Biomedical engineering online. 2014 Dec;13(1):1-3. View at Publisher | View at Google Scholar | View at Indexing
  • Lim JY, Leech M. Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck. Acta Oncologica. 55,799-806 (2016). View at Publisher | View at Google Scholar
  • Fishman EK. Introducing Imaging in Medicine. Imaging in Medicine. 1,1 (2009). View at Publisher | View at Google Scholar
  • Cruz-Roa A, Caicedo JC, González FA. Visual pattern mining in histology image collections using bag of features. Artificial intelligence in medicine. 2011 Jun 1;52(2):91-106. View at Publisher | View at Google Scholar | View at Indexing
  • Ali S, Madabhushi A. An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE transactions on medical imaging. 31,1448-1460 (2012). View at Publisher | View at Google Scholar | View at Indexing
  • Madabhushi A, Agner S, Basavanhally A, et al. Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. Computerized medical imaging and graphics. 35, 506-514 (2011).

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  • Kwak JT, Hewitt SM, Sinha S, Bhargava R. Multimodal microscopy for automated histologic analysis of prostate cancer. BMC cancer. 2011 Dec;11(1):1-6. View at Publisher | View at Google Scholar | View at Indexing
  • Xu Y, Zhu JY, Chang E, Tu Z. Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering. In2012 IEEE Conference on Computer Vision and Pattern Recognition 2012 Jun 16 (pp. 964-971). IEEE. View at Publisher | View at Google Scholar | View at Indexing
  • 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
  • Xu Y, Zhu JY, Eric I, et al. Weakly supervised histopathology cancer image segmentation and classification. Medical image analysis. 18,591-604 (2014). View at Publisher | View at Google Scholar | View at Indexing
  • Di Cataldo S, Bottino A, Islam IU, et al. Subclass discriminant analysis of morphological and textural features for HEp-2 staining pattern classification. Pattern Recognition. 47,2389-2399 (2014). View at Publisher | View at Google Scholar
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  • Janowczyk A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman MD, Madabhushi A. High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts. IEEE transactions on biomedical engineering. 59,1240-1252 (2012). View at Publisher | View at Google Scholar
  • Hipp J, Flotte T, Monaco J, Cheng J, Madabhushi A, Yagi Y, Rodriguez-Canales J, Emmert-Buck M, Dugan MC, Hewitt S, Toner M. Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess. Journal of pathology informatics. 2,25(2011). View at Publisher | View at Google Scholar
  • Srinivas U, Mousavi HS, Monga V, et al. Simultaneous sparsity model for histopathological image representation and classification. IEEE transactions on medical imaging. 33,1163-1179 (2014). View at Publisher | View at Google Scholar | View at Indexing
  • Xu J, Janowczyk A, Chandran S, Madabhushi A. A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation. InSPIE Medical Imaging. International Society for Optics and Photonics. 76230Y-76230Y (2010). View at Publisher | View at Google Scholar
  • 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
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  • 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 | View at Indexing
  • Srinivas U, Mousavi H, Jeon C, Monga V, Hattel A, Jayarao B. SHIRC: A simultaneous sparsity model for histopathological image representation and classification. InBiomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on 2013 Apr 7. 1118-1121 View at Publisher | View at Google Scholar
  • Xu Y, Zhang J, Eric I, et al. Context-constrained multiple instance learning for histopathology image segmentation. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2012 Oct 1, 623-630. Springer Berlin Heidelberg. View at Publisher | View at Google Scholar
  • 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
  • Bueno G, Gonzalez R, Déniz O, et al. A parallel solution for high resolution histological image analysis. Computer methods and programs in biomedicine. 108,388-401 (2012). View at Publisher | View at Google Scholar | View at Indexing
  • 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 | View at Indexing
  • Ramos-Pollán R, González FA, Caicedo JC, et al. Bigs: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources. InE-science (e-science), 2012 IEEE 8th International Conference IEEE. 1-8 (2012) View at Publisher | View at Google Scholar
  • Gough A, Lezon T, Faeder JR, Chennubhotla C, Murphy RF, Critchley-Thorne R, Taylor DL. High content analysis with cellular and tissue systems biology: a bridge between cancer cell biology and tissue-based diagnostics. The molecular basis of cancer. 4 (2014). View at Publisher | View at Google Scholar
  • Madabhushi A, Basavanhally A, Doyle S, Agner S, Lee G. Computer-aided prognosis: predicting patient and disease outcome via multi-modal image analysis. InBiomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium IEEE. 1415-1418 (2010). View at Publisher | View at Google Scholar | View at Indexing
  • Hipp J, Cheng J, Pantanowitz L, et al. Image microarrays (IMA): Digital pathology's missing tool. Journal of pathology informatics. 2,47 (2011). View at Publisher | View at Google Scholar | View at Indexing
  • 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
  • Sanchez V, Aulí-Llinàs F, Bartrina-Rapesta J, Serra-Sagristà J. HEVC-based lossless compression of Whole Slide pathology images. InSignal and Information Processing (GlobalSIP) IEEE Global Conference. IEEE 297-301 (2014) View at Publisher | View at Google Scholar
  • Sparks R, Madabhushi A. Gleason grading of prostate histology utilizing manifold regularization via statistical shape model of manifolds. InSPIE Medical Imaging International Society for Optics and Photonics. 83151J-83151J,(2012). View at Publisher | View at Google Scholar
  • Hipp JD, Fernandez A, Compton CC, Balis UJ. Why a pathology image should not be considered as a radiology image. Journal of pathology informatics. 2011;2. View at Publisher | View at Google Scholar | View at Indexing
  • Hipp J, Monaco J, Kunju LP, et al. Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification. Analytical cellular pathology. 35,251-265 (2012). View at Publisher | View at Google Scholar | View at Indexing
  • 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 | View at Indexing
  • Held C, Nattkemper T, Palmisano R, et al. Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis. Journal of pathology informatics. 4 (2013). View at Publisher | View at Google Scholar
  • Kumar R, Srivastava R, Srivastava S. Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features. Journal of medical engineering. (2015). View at Publisher | View at Google Scholar
  • Oztan B, Shubert KR, Bjornsson CS, Plopper GE, Yener B. Biologically-driven cell-graphs for breast tissue grading. InBiomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on 2013 Apr 7 (pp. 137-140). IEEE. View at Publisher | View at Google Scholar
  • Wu G, Zhao X, Luo S, et al. Histological image segmentation using fast mean shift clustering method. Biomedical engineering online. 14,24 (2015). View at Publisher | View at Google Scholar | View at Indexing
  • 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 | View at Indexing
  • 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 | View at Indexing
  • Bueno G, Déniz O, Salido J, et al. Colour model analysis for histopathology image processing. InColor medical image analysis 2013 (pp. 165-180). Springer Netherlands. View at Publisher | View at Google Scholar

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Google Scholar citation report
Citations : 4878

Imaging in Medicine received 4878 citations as per Google Scholar report


Imaging in Medicine peer review process verified at publons

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