Joint Kernel-Based Supervised Hashing for Scalable Histopathological Image Analysis
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CBR systems have an advantage over traditional classifier-based systems in enabling investigators to interrogate data while visualizing the most relevant profiles.
“One of the advantages of CBR systems over traditional classifier-based systems is that they enable investigators to interrogate data while visualizing the most relevant profiles [254].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [254]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Supervised hashing with kernels
Liu, W., Wang, J., Ji, R., Jiang, Y., & Chang, S. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247912 · Cited by 1,418 · see citing works
Breast Cancer Histopathology Image Analysis: A Review
Veta, M., Pluim, J. P. W., van Diest, P. J., & Viergever, M. A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2303852 · Cited by 696 · see citing works
Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval
Zhang, X., Liu, W., Dündar, M., Badve, S., & Zhang, S. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2361481 · Cited by 224 · see citing works
Self-citationComputerized classification of intraductal breast lesions using histopathological images
Dündar, M., Badve, S., Bilgin, G., Raykar, V. C., Jain, R., Sertel, O., & Gürcan, M. N. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2110648 · Cited by 194 · see citing works
Histopathology Image Classification Using Bag of Features and Kernel Functions
Caicedo, J. C., Cruz, A., & Gonzalez, F. A. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-02976-9_17 · Cited by 192 · see citing works
Scalable similarity search with optimized kernel hashing
He, J., Liu, W., & Chang, S. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835946 · Cited by 150 · see citing works
Content-Based Microscopic Image Retrieval System for Multi-Image Queries
Akakın, H. Ç., & Gürcan, M. N. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2185829 · Cited by 122 · see citing works
Computer-Aided Diagnosis of Mammographic Masses Using Scalable Image Retrieval
Jiang, M., Zhang, S., Li, H., & Metaxas, D. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2365494 · Cited by 114 · see citing works
Self-citationMultiple feature kernel hashing for large-scale visual search
Liu, X., He, J., & Lang, B. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.08.022 · Cited by 94 · see citing works
Content-based histopathology image retrieval using a kernel-based semantic annotation framework
Caicedo, J. C., Gonzalez, F. A., & Romero, E. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.01.011 · Cited by 58 · see citing works
Mammogram retrieval through machine learning within BI-RADS standards
Wei, C., Li, Y., & Huang, P. J. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.01.012 · Cited by 52 · see citing works
Mining Histopathological Images via Composite Hashing and Online Learning
Zhang, X., Yang, L., Liu, W., Su, H., & Zhang, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10470-6_60 · Cited by 28 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Navab, N., Joachim Hornegger, A., Wells, W. M., & Frangi, A. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9 · Cited by 2,288 · see citing works
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works
“One of the advantages of CBR systems over traditional classifier-based systems is that they enable investigators to interrogate data while visualizing the most relevant profiles [254].”
Large-scale retrieval for medical image analytics: A comprehensive review
Li, Z., Zhang, X., Müller, H., & Zhang, S. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.09.007 · Cited by 259 · see citing works
“Jiang et al. (2015c) successfully solved the scalable mammo-gram retrieval problem based on a vocabulary tree with adaptive weighting.”
BackgroundMethodInfluentialSelf-citationWan, T., Cao, J., Chen, J., & Qin, Z. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.05.084 · Cited by 132 · see citing works
Pairwise based deep ranking hashing for histopathology image classification and retrieval
Shi, X., Sapkota, M., Xing, F., Liu, F., Cui, L., & Yang, L. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.015 · Cited by 93 · see citing works
Self-citationShi, J., Wu, J., Li, Y., Zhang, Q., & Ying, S. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2602823 · Cited by 80 · see citing works
Bioimage Classification with Handcrafted and Learned Features
Nanni, L., Brahnam, S., Ghidoni, S., & Lumini, A. (2019) · IEEE/ACM Transactions on Computational Biology & Bioinformatics · doi:10.1109/tcbb.2018.2821127 · Cited by 39 · see citing works
“patterns (LBP) [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34].”
Supervised graph hashing for histopathology image retrieval and classification
Shi, X., Xing, F., Xu, K., Xie, Y., Su, H., & Yang, L. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.009 · Cited by 44 · see citing works
Self-citationDeep Graph-Based Multimodal Feature Embedding for Endomicroscopy Image Retrieval
Gu, Y., Vyas, K., Shen, M., Yang, J., & Yang, G. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2980129 · Cited by 43 · see citing works
“[3] extended their previous work [19] by adding more types of features and kernels for improved performance; and recently, Gu et al.”
MethodScalable histopathological image analysis via supervised hashing with multiple features
Jiang, M., Zhang, S., Huang, J., Yang, L., & Metaxas, D. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.07.011 · Cited by 40 · see citing works
Self-citationLow Dimensional Representation of Fisher Vectors for Microscopy Image Classification
Song, Y., Li, Q., Huang, H., Feng, D. D., Chen, M., & Cai, W. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2687466 · Cited by 39 · see citing works
“detecting or quantifying a particular biomarker and such methods are typically evaluated on a variety of different applications [6]–[14].”
Densely-Connected Multi-Magnification Hashing for Histopathological Image Retrieval
Gu, Y., & Yang, J. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2882647 · Cited by 35 · see citing works
Sparks, R. E., & Madabhushi, A. (2016) · Scientific Reports · doi:10.1038/srep27306 · Cited by 27 · see citing works
Song, Y., Cai, W., Huang, H., Feng, D. D., Wang, Y., & Chen, M. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1318-9 · Cited by 25 · see citing works
Multi-level magnification correlation hashing for scalable histopathological image retrieval
Gu, Y., & Yang, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.050 · Cited by 22 · see citing works
Large-scale image retrieval with supervised sparse hashing
Xu, Y. L., Shen, F., Xu, X., Gao, L., Wang, Y., & Tan, X. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.05.109 · Cited by 21 · see citing works
“Depart from most these methods focusing generic image retrieval, a few works [17,57,58] have been devoted to tackle the large-scale medical image retrieval problem.”
BackgroundIndexing and mining large-scale neuron databases using maximum inner product search
Li, Z., Fang, R., Shen, F., Katouzian, A., & Zhang, S. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.09.041 · Cited by 19 · see citing works
Self-citationRapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods
Pan, L., Qiang, Y., Yuan, J., & Wu, L. (2016) · BioMed Research International · doi:10.1155/2016/3162649 · Cited by 12 · see citing works
“Jiang et al. [15] used a joint kernel-based supervised hashing algorithm with a small amount of supervised information to compress breast histopathological images into 10-bit hash codes and identified actionable and benign tumors based on the retrieval results.”
BackgroundCell Encoding for Histopathology Image Classification
Shi, X., Xing, F., Xie, Y., Su, H., & Yang, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_4 · Cited by 11 · see citing works
“Some supervised hashing algorithms [6,7,19,20] have shown superior classification performance to traditional methods, however, they usually have one or two following major limitations: (i) Directly encoding the whole pathology image rather than individual cells into binary codes for image classification might fail to…”
BackgroundSelf-citationAn Efficient Supervised Deep Hashing Method for Image Retrieval
Hussain, A., Li, H.-C., Ali, M., Wali, S., Hussain, M., & Rehman, A. (2022) · Entropy · doi:10.3390/e24101425 · Cited by 11 · see citing works
An exploratory analysis approach for understanding variation in stochastic textured surfaces
Bui, A. T., & Apley, D. W. (2019) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2019.01.019 · Cited by 10 · see citing works
Visual feature representation in microscopy image classification
Song, Y., & Cai, W. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-814972-0.00004-7 · Cited by 9 · see citing works
Xiao, X., Qiang, Z., Zhao, J., Qiang, Y., Wang, P., & Han, P. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-7041-y · Cited by 7 · see citing works
“After statistical analysis, it was found that the window of the high attenuation range highlights the texture features and the low attenuation range highlights the edge features [10].”
BackgroundDetecting Mammographic Masses via Image Retrieval and Discriminative Learning
Jiang, M., Zhang, S., & Metaxas, D. (2018) · Intelligent systems reference library · doi:10.1007/978-3-319-68843-5_5 · Cited by 5 · see citing works
Self-citationHashing-based large-scale medical image retrieval for computer-aided diagnosis
Zhang, X., & Zhang, S. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804076-8.00008-6 · Cited by 1 · see citing works
Self-citationDual discriminative local coding for tissue aging analysis
Song, Y., Li, Q., Zhang, F., Huang, H., Feng, D. D., Wang, Y., Chen, M., & Cai, W. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.001 · Cited by 1 · see citing works
“…on feature extraction, in which customized features are designed (Su et al., 2012; Sparks and Madabhushi, 2013; Peter et al., 2015; Xu et al., 2015; Jiang et al., 2015; Barker et al., 2016) or automated feature learning is conducted (Zhou et al., 2014; Otalora et al., 2015; BenTaieb et al., 2015;…”
Deep Hashing and Its Application for Histopathology Image Analysis
Shi, X., & Yang, L. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-13969-8_11
Self-citation
- Authors
- Jiang, M., Zhang, S., Huang, J., Yang, L., & Metaxas, D. N.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- University of North Carolina at Charlotte (US)
- The University of Texas at Arlington (US)
- University of Florida (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 366-373
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2202639555
- Cited by
- 27
- References
- 15
- Citations by year
- 2022: 1 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 5 · 2016: 9 · 2015: 1
- FWCI
- 2.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- supervised hashing, breast cancer diagnosis, feature fusion, semantic gap, content-based image retrieval, case-based reasoning, binary hashing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
