Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images

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Learning for Cancer Tissue Classification in Digital Histopathology Images</title> <meta name="description" content="Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images"> <meta name="keywords" content="Attention Multiple Instance Learning, Multiple Instance Learning, transfer learning, histopathological slides, cancer tissue classification."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images"> <meta name="citation_author" content="Afaf Alharbi"> <meta name="citation_author" content="Qianni Zhang"> <meta name="citation_publication_date" content="2024/03/28"> <meta name="citation_journal_title" content="International Journal of Medical and Health Sciences"> <meta name="citation_volume" content="18"> <meta 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class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Afaf%20Alharbi">Afaf Alharbi</a>, <a href="https://publications.waset.org/search?q=Qianni%20Zhang"> Qianni Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper presents a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network-based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation on an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.</p> <iframe src="https://publications.waset.org/10013578.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Attention%20Multiple%20Instance%20Learning" title="Attention Multiple Instance Learning">Attention Multiple Instance Learning</a>, <a href="https://publications.waset.org/search?q=Multiple%0D%0AInstance%20Learning" title=" Multiple Instance Learning"> Multiple Instance Learning</a>, <a href="https://publications.waset.org/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/search?q=histopathological%20slides" title=" histopathological slides"> histopathological slides</a>, <a href="https://publications.waset.org/search?q=cancer%0D%0Atissue%20classification." title=" cancer tissue classification."> cancer tissue classification.</a> </p> <a href="https://publications.waset.org/10013578/attention-multiple-instance-learning-for-cancer-tissue-classification-in-digital-histopathology-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013578/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013578/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013578/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013578/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013578/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013578/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013578/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013578/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013578/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013578/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013578.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">221</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Thomas G. Dietterich a, Richard H. Lathrop band Lozano-P´erez, Tom´as, ”Solving the multiple instance problem with axis-parallel rectangles”,Artificial intelligence,Elsevier. vol.89,pp. 31–71,1997. <br>[2] Maron, Oded and Lozano-P´erez, Tom´as,”A framework for multiple-instance learning”, Advances in neural information processing systems, Citeseer,pp.570–576,1998. <br>[3] Cosatto, Eric and Laquerre, Pierre-Francois and Malon, Christopher and Graf, Hans-Peter and Saito, Akira and Kiyuna, Tomoharu and Marugame, Atsushi and Kamijo, Ken’ichi,” Automated gastric cancer diagnosis on h&e-stained sections; ltraining a classifier on a large scale with multiple instance machine learning”, Medical Imaging 2013: Digital Pathology, International Society for Optics and Photonics, vol. 8676, pp.867–605, 2013. <br>[4] liu2012key,Liu, Guoqing and Wu, Jianxin and Zhou, Zhi-Hua,”Key instance detection in multi-instance learning” ,Asian Conference on Machine Learning,PMLR, pp.253–268,2012. <br>[5] Cheplygina, Veronika and Sørensen, Lauge and Tax, David MJ and de Bruijne, Marleen and Loog, Marco, ”Label stability in multiple instance learning”,International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.539–546,Springer, 2015. <br>[6] Andrews, Stuart and Tsochantaridis, Ioannis and Hofmann, Thomas, ”Support vector machines for multiple-instance learning”,Advances in neural information processing systems,vol. 15, MIT; 1998,2003. <br>[7] Chen, Yixin and Bi, Jinbo and Wang, James Ze,”Multiple-instance learning via embedded instance selection”,IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28,pp. 1931–1947, IEEE,2006. <br>[8] Ramon, Jan and De Raedt, Luc,”Multi instance neural networks”,Proceedings of the ICML-2000 workshop on attribute-value and relational learning, pp. 53–60,2000. <br>[9] Raykar, Vikas C and Krishnapuram, Balaji and Bi, Jinbo and Dundar, Murat and Rao, R Bharat, ”Bayesian multiple instance learning: automatic feature selection and inductive transfer”,Proceedings of the 25th international conference on Machine learning, pp. 808–815,2008. <br>[10] Zhang, Cha and Platt, John and Viola, Paul,”Multiple instance boosting for object detection”, Advances in neural information processing systems, vol. 18, pp. 1417–1424, Citeseer, 2005. <br>[11] Kandemir, Melih and Hamprecht, Fred A,”Computer-aided diagnosis from weak supervision: A benchmarking study”, Computerized medical imaging and graphics, vol. 42, Elsevier, 2015. <br>[12] Sirinukunwattana, Korsuk and Raza, Shan E Ahmed and Tsang, Yee-Wah and Snead, David RJ and Cree, Ian A and Rajpoot, Nasir M,”Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images”,IEEE transactions on medical imaging,vol. 35, pp. 1196–1206, IEEE, 2016. <br>[13] Glorot, Xavier and Bengio, Yoshua,”Understanding the difficulty of training deep feedforward neural networks”,Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256, JMLR Workshop and Conference Proceedings, 2010. <br>[14] Wang, Xinggang and Yan, Yongluan and Tang, Peng and Bai, Xiang and Liu, Wenyu,”Revisiting Multiple Instance Neural Networks”, arXiv preprint arXiv:1610.02501, 2016. <br>[15] LeCun, Yann and Bottou, L´eon and Bengio, Yoshua and Haffner, Patrick,”Gradient-based learning applied to document recognition”,Proceedings of the IEEE,vol. 86, pp. 2278–2324, IEEE, 1998. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images