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Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images [article]

Edwin Yuan, Junkyo Suh
<span title="2018-11-09">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We then train a neural network to segment images of nuclei from the H&E images.  ...  In this paper, we develop a complete pipeline for stain normalization, segmentation, and classification of nuclei in hematoxylin and eosin (H&E) stained breast cancer histopathology images.  ...  We also thank Magda for assisting our understanding of histological sections, and breast histology cell-types, and Korsuk for providing the segmentation codebase and technical support regarding it.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.03815v1">arXiv:1811.03815v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xx3q53vkmvazrpnegjls7fs3by">fatcat:xx3q53vkmvazrpnegjls7fs3by</a> </span>
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Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
<span title="2019-12-28">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
learning and various other sub-variants of these methods.  ...  In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  et al. (2017) Breast H&E Breast cancer classification Stacked CNN incorporating contextual information Private set -221 images Agarwalla et al. (2017) Breast H&E tumour segmentation CNN +  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.12378v1">arXiv:1912.12378v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xdfkzzwzb5alhjfhffqpcurb2u">fatcat:xdfkzzwzb5alhjfhffqpcurb2u</a> </span>
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On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

Carmelo Militello, Andrea Ranieri, Leonardo Rundo, Ildebrando D'Angelo, Franco Marinozzi, Tommaso Vincenzo Bartolotta, Fabiano Bini, Giorgio Russo
<span title="2021-12-24">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region  ...  metrics—Mean Absolute Distance (MAD), Maximum Distance (MaxD), Hausdorff Distance (HD)—encourage the use of unsupervised machine learning techniques in medical image segmentation.  ...  .; Palus, H.; Borys, D.; Psiuk-Maksymowicz, K. Breast lesion segmentation in DCE-MRI Imaging.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app12010162">doi:10.3390/app12010162</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u5cycszgvnbi7ithuobcr2dtuu">fatcat:u5cycszgvnbi7ithuobcr2dtuu</a> </span>
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Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation [article]

Hugo Oliveira, Edemir Ferreira, Jefersson A. dos Santos
<span title="2019-12-07">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In order to tackle this problem, in this work we propose an Unsupervised and Semi-Supervised Domain Adaptation method for segmentation of biomedical images using Generative Adversarial Networks for Unsupervised  ...  Therefore an important step in improving the generalization capabilities of these methods is to perform Unsupervised and Semi-Supervised Domain Adaptation between different datasets of biomedical images  ...  We also thank CAPES, CNPq (424700/2018-2), and FAPEMIG (APQ-00449-17) for the financial support  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.05553v4">arXiv:1901.05553v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/43f4nbvt6zek3oyezxjuz5zjvq">fatcat:43f4nbvt6zek3oyezxjuz5zjvq</a> </span>
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Unsupervised Texture Segmentation [chapter]

Michal Haindl, Stanislav Mikes
<span title="2008-11-01">2008</span> <i title="InTech"> Pattern Recognition Techniques, Technology and Applications </i> &nbsp;
This research was supported by the projects GAČR 102/08/0593, 1ET400750407 of the Grant Agency of the Academy of Sciences CR and partially by the MŠMT grants 1M0572, 2C06019. References  ...  The goal of the benchmark is to produce score, performance and quality measures for an algorithm's performance for two main reasons : S o t h a t d i f f e r e n t a l g o r i t h m s c a n b e compared  ...  Alternatively our segmenters can be used to detect meaningful areas in large remote sensing images and in various other image segmentation applications. www.intechopen.com Unsupervised Texture Segmentation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/6243">doi:10.5772/6243</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rif6clwjwfg2nj2fwxsjolnh2u">fatcat:rif6clwjwfg2nj2fwxsjolnh2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190503071623/https://cdn.intechopen.com/pdfs/5787.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/bb/85bb2fd1cb3ecd583ec0470adbb0bff8fb8c7b42.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/6243"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Novel Color Reduction Based Image Segmentation Technique For Detection Of Cancerous Region in Breast Thermograms

Amin Habibi, Mousa Shamsi
<span title="2015-12-19">2015</span> <i title="Universidad Federal de Santa Maria"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/27fiql2vmfd2xchuld2y3o3tte" style="color: black;">Ciência e Natura</a> </i> &nbsp;
In this article an important application of image processing in determination of Breast Cancer is studied, and A Novel Image Segmentation Technique is proposed in order to determine Cancer in Breast Thermograms  ...  Segmentation of an image into its components plays an important role in most of the image processing applications.  ...  Moreover, our proposed method is of type unsupervised . Figure 2 2 shows flowchart of the proposed algorithm. For color image segmentation converted from RGB to color space HSV.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5902/2179460x20799">doi:10.5902/2179460x20799</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mqvyhzc7lrfo7jf5ijnorog52i">fatcat:mqvyhzc7lrfo7jf5ijnorog52i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922020448/https://periodicos.ufsm.br/cienciaenatura/article/download/20799/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/75/9c/759c5682cae0ab6f95a80f07cd61cdb84f273571.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5902/2179460x20799"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Tour of Unsupervised Deep Learning for Medical Image Analysis [article]

Khalid Raza, Nripendra Kumar Singh
<span title="2018-12-19">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical imaging and image analysis.  ...  Future research opportunities and challenges of unsupervised techniques for medical image analysis have also been discussed.  ...  Almas Jabeen, and Mr. Nisar Wani for necessary support. Conflict of Interest Statement Authors declare that there is no any conflict of interest in the publication of this manuscript.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.07715v1">arXiv:1812.07715v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4dd75wfhvnf7db3v72575tikoi">fatcat:4dd75wfhvnf7db3v72575tikoi</a> </span>
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Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
<span title="2021-04-06">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7inqmh346zfjjizjyieh7ijtma" style="color: black;">Multimedia tools and applications</a> </i> &nbsp;
It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA.  ...  Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images.  ...  segmentation challenge (33 images) Dice coefficient Van Eycke et al. (2018) [156] H & E Integration of DCAN, UNet, and ResNet models Segmentation of glandular epithelium in H & E and IHC  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-021-10707-4">doi:10.1007/s11042-021-10707-4</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33841033">pmid:33841033</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8023554/">pmcid:PMC8023554</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cm522go4nbdbnglgzpw4nu7tbi">fatcat:cm522go4nbdbnglgzpw4nu7tbi</a> </span>
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Robust segmentation using kernel and spatial based fuzzy c-means methods on breast x-ray images

Xuejun Sun, Dmitry Goldgof, Walker Land, Joseph M. Reinhardt, Josien P. W. Pluim
<span title="2008-03-06">2008</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ha25cznnjncxtjoykhsg6fz5ly" style="color: black;">Medical Imaging 2008: Image Processing</a> </i> &nbsp;
Robust methods for precise segmentation of breast region or volume from breast X-ray images, including mammogram and tomosynthetic image, is crucial for applications of these medical images.  ...  This paper proposes and develops robust fuzzy c-means (FCM) segmentation methods for segmentation of breast region on breast x-ray images, including mammography and tomosynthesis, respectively.  ...  Komen Breast Cancer Foundation, grant No. BCTR0600283.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.770711">doi:10.1117/12.770711</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/miip/SunGL08.html">dblp:conf/miip/SunGL08</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nxjqyhdo6bc3bh2joivetbwxmm">fatcat:nxjqyhdo6bc3bh2joivetbwxmm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190308210544/http://pdfs.semanticscholar.org/f97a/c67f8b89ffdb62068d5c00f34af7f7126fe3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f9/7a/f97ac67f8b89ffdb62068d5c00f34af7f7126fe3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.770711"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation

Hugo Oliveira, Edemir Ferreira, Jefersson A. Dos Santos
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
We conducted experiments to compare our method with traditional and state-of-the-art baselines by using several domains, datasets, and segmentation tasks.  ...  of biomedical images.  ...  ACKNOWLEDGMENT The authors would like to thank NVIDIA for the donation of the GPUs that allowed the execution of all experiments in this artice.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2991688">doi:10.1109/access.2020.2991688</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jqascltn7zft3a5g5tiagnjsgi">fatcat:jqascltn7zft3a5g5tiagnjsgi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108152021/https://ieeexplore.ieee.org/ielx7/6287639/8948470/09084116.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/8b/c88b3050acc8be9b3fe52c4e5ace0a75d4b2a0ce.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2991688"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images [chapter]

Vincent Roullier, Olivier Lézoray, Vinh-Thong Ta, Abderrahim Elmoataz
<span title="">2010</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images.  ...  The proposed segmentation is fully unsupervised by using domain specific knowledge.  ...  In this work, we present a graph-based multi-resolution segmentation and analysis strategy for histological breast cancer whole slide images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-17289-2_52">doi:10.1007/978-3-642-17289-2_52</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kgsynmt2jzcnpetwbnxvmzqnfi">fatcat:kgsynmt2jzcnpetwbnxvmzqnfi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706044842/https://lezoray.users.greyc.fr/Publis/Roullier_ISVC2010.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e9/ae/e9aef0d4418d47d5eb6557269ccd7cb9a8f19fff.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-17289-2_52"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Graph-based multi-resolution segmentation of histological whole slide images

V. Roullier, V-T. Ta, O. Lezoray, A. Elmoataz
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qcepwfkflvg5toaa6fh2alj3b4" style="color: black;">2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro</a> </i> &nbsp;
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images.  ...  The proposed segmentation is fully unsupervised by using domain specific knowledge.  ...  We consider that a graph G = (V, E, w) and a function f ∈ H(V ) are given.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isbi.2010.5490390">doi:10.1109/isbi.2010.5490390</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/isbi/RoullierTLE10.html">dblp:conf/isbi/RoullierTLE10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wjr6n3q7bbfchjxd4tzsbkalfi">fatcat:wjr6n3q7bbfchjxd4tzsbkalfi</a> </span>
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Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation

Anirudh Choudhary, Li Tong, Yuanda Zhu, May D. Wang
<span title="2020-08-21">2020</span> <i title="Georg Thieme Verlag KG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/25ybzim6mvglde3zgjvka7ehii" style="color: black;">IMIA Yearbook of Medical Informatics</a> </i> &nbsp;
We highlighted the role of unsupervised DA in image segmentation and described opportunities for future development.  ...  Using adversarial techniques, unsupervised DA has achieved good performance, especially for segmentation tasks.  ...  S WSI (CK → PD-L1) Lung Unsupervised Kapil et. al., [101] Unidirectional S WSI (H&E ↔ IF) Breast, Bladder, Lung Brieu et. al., [16] LFST Adversarial C WSI → Microscopy Colon Zhang et.  ... 
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Unsupervised morphological segmentation of tissue compartments in histopathological images

Shereen Fouad, David Randell, Antony Galton, Hisham Mehanna, Gabriel Landini, Dalin Tang
<span title="2017-11-30">2017</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis.  ...  Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions.  ...  Acknowledgments This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 "Novel context-based segmentation algorithms for intelligent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0188717">doi:10.1371/journal.pone.0188717</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29190786">pmid:29190786</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5708642/">pmcid:PMC5708642</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qglv3gidpzdzjnenjduw5qcf3y">fatcat:qglv3gidpzdzjnenjduw5qcf3y</a> </span>
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833 A scalable deep learning framework for rapid automated annotation of histologic and morphologic features from large unlabeled pan-cancer H&E datasets

David Soong, David Soong, David Soong, Anantharaman Muthuswamy, Clifton Drew, Nora Pencheva, Maria Jure-Kunkel, Kate Sasser, Hisham Hamadeh, Suzana Couto, Brandon Higgs
<span title="">2021</span> <i title="BMJ"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tcu4zb76ybaefkex5wp4pzcq3u" style="color: black;">Journal for ImmunoTherapy of Cancer</a> </i> &nbsp;
quality and speed of analytical workflows aimed at deriving clinically relevant features.MethodsThe dataset consisted of >200 H&E images across >10 solid tumor types (e.g. breast, lung, colorectal, cervical  ...  As an extension of this classifier framework, all whole slide H&E images were segmented and composite lymphocyte, stromal, and necrotic content per patient tumor was derived and correlated with estimates  ...  Methods The dataset consisted of >200 H&E images across >10 solid tumor types (e.g. breast, lung, colorectal, cervical, and urothelial cancers) from advanced disease patients.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1136/jitc-2021-sitc2021.833">doi:10.1136/jitc-2021-sitc2021.833</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p6pherhg4ngffaziyl5dlfcbpi">fatcat:p6pherhg4ngffaziyl5dlfcbpi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211114065527/https://jitc.bmj.com/content/jitc/9/Suppl_2/A874.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/99/a0/99a0eb54225e6abd6a70b73741a67ce436eef670.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1136/jitc-2021-sitc2021.833"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> bmj.org </button> </a>
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