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A comprehensive framework for classification of nuclei in digital microscopy imaging: An application to diffuse gliomas

Jun Kong, Lee Cooper, Fusheng Wang, Candace Chisolm, Carlos Moreno, Tahsin Kurc, Patrick Widener, Daniel Brat, Joel Saltz
2011 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
In this paper, we present a comprehensive framework to support classification of nuclei in digital microscopy images of diffuse gliomas.  ...  In our study, 2770 nuclei of six types are annotated by neuropathologists from 29 wholeslide images of glioma biopsies.  ...  Acknowledgments This research is supported in part by Grant Number R01LM009239 from the NLM, by NCI Contract No.  ... 
doi:10.1109/isbi.2011.5872833 pmid:22249771 pmcid:PMC3256584 dblp:conf/isbi/KongCWCMKWBS11 fatcat:dxcvso5td5ctdejtu2eohtzqku

Histopathological Image Analysis: A Review

M.N. Gurcan, L.E. Boucheron, A. Can, A. Madabhushi, N.M. Rajpoot, B. Yener
2009 IEEE Reviews in Biomedical Engineering  
Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis  ...  This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.  ...  The motivation of this paper is to present a comprehensive review of the state-of-the-art CAD methods and the techniques employed for automated image analysis of digitized histopathology imagery.  ... 
doi:10.1109/rbme.2009.2034865 pmid:20671804 pmcid:PMC2910932 fatcat:a6sm4iy5gffbhlc23dtlp7xe2q

AI and Medical Imaging Informatics: Current Challenges and Future Directions

Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita (+4 others)
2020 IEEE journal of biomedical and health informatics  
for both radiology and digital pathology applications.  ...  It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already  ...  These approaches aim to identify voxels in an input image that are important for classification based on computing the gradient of a given neuron at a fixed layer with respect to voxels in the input image  ... 
doi:10.1109/jbhi.2020.2991043 pmid:32609615 pmcid:PMC8580417 fatcat:dcaefxwwqjfwla5asin34x2hxm

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Adrien Depeursinge, Antonio Foncubierta-Rodriguez, Dimitri Van De Ville, Henning Müller
2014 Medical Image Analysis  
In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms  ...  The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices.  ...  Acknowledgments This work was supported by the Swiss National Science Foundation (under Grants 205320-141300/1, PBGEP2_142283 and PP00P2-146318), the CIBM, and the EU 7th Framework Program in the context  ... 
doi:10.1016/ pmid:24231667 fatcat:6luivlhrtrdv5dlbh626ohkps4

Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification

Yang Song, Qing Li, Heng Huang, Dagan Feng, Mei Chen, Weidong Cai
2017 IEEE Transactions on Medical Imaging  
To this end, in this study we propose a new feature representation algorithm to facilitate automated microscopy image classification.  ...  Microscopy image classification is important in various biomedical applications, such as cancer subtype identification and protein localization for high content screening.  ...  Our aim of this study belongs to this type, i.e. to design an imagelevel classification method for varying microscopy imaging applications without detecting biomarkers.  ... 
doi:10.1109/tmi.2017.2687466 pmid:28358678 fatcat:rq76wyzscbgl3j3ajpjvqrsnmu

Machine-Based Morphologic Analysis of Glioblastoma Using Whole-Slide Pathology Images Uncovers Clinically Relevant Molecular Correlates

Jun Kong, Lee A. D. Cooper, Fusheng Wang, Jingjing Gao, George Teodoro, Lisa Scarpace, Tom Mikkelsen, Matthew J. Schniederjan, Carlos S. Moreno, Joel H. Saltz, Daniel J. Brat, Miguel A Andrade-Navarro
2013 PLoS ONE  
In this paper, we present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups  ...  Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and  ...  Overall, the analysis framework presented provides a generic approach for large-scale microscopy images and for comprehensive correlative investigations using complementary disease data.  ... 
doi:10.1371/journal.pone.0081049 pmid:24236209 pmcid:PMC3827469 fatcat:6zpfrcf5bzeu3mguswwzkfgf5m

An overview of deep learning in medical imaging [article]

Imran Ul Haq
2022 arXiv   pre-print
, and registration), (iii) review seven main application fields of DL in medical imaging, (iv) give an initial stage to those keen on adding to the research area about DL in clinical imaging by providing  ...  This success started in 2012 when an ML model accomplished a remarkable triumph in the ImageNet Classification, the world's most famous competition for computer vision.  ...  Acknowledgment: We have no funding for our review article. REFERENCES:  ... 
arXiv:2202.08546v1 fatcat:tg32btcm5vdsnlzeuhdttozj6m

Multiplexed imaging for diagnosis and therapy

Kathrin Heinzmann, Lukas M. Carter, Jason S. Lewis, Eric O. Aboagye
2017 Nature Biomedical Engineering  
However, for the imaging data to accurately represent a complex fingerprint, the individual imaging parameters need to be measured and analysed in relation to their wider spatial and molecular context.  ...  Common phenotypes can be detected by in vivo imaging technologies, and effectively define the emerging standards for disease classification and patient stratification in radiology.  ...  The authors at Memorial Sloan Kettering Cancer Center would like to acknowledge the National Institutes of Health for financial support, the generous support of The Mr. William H. and Mrs.  ... 
doi:10.1038/s41551-017-0131-8 fatcat:4mfo3e7ytnetjb3wbl6yjgp44m

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
PET Reconstruction 520 Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to Early Dementia 528 IMAGE SEGMENTATION AND CLASSIFICATION  ...  491 An Open Framework Enabling Electromagnetic Tracking in Image-Guided Interventions 492 Small Lesion Classication in Dynamic Contrast Enhancement MRI for Breast Cancer Early Detection 494 Uncertainty  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma

Islam Alzoubi, Guoqing Bao, Rong Zhang, Christina Loh, Yuqi Zheng, Svetlana Cherepanoff, Gary Gracie, Maggie Lee, Michael Kuligowski, Kimberley L. Alexander, Michael E. Buckland, Xiuying Wang (+1 others)
2022 Cancers  
Our present study extends the applicability of the PathoFusion framework to the cellular level.  ...  PathoFusion has the potential to be applied to additional problems that seek to correlate heterogeneous data streams and to serve as a clinically applicable, weakly supervised system for histological image  ...  Acknowledgments: The authors acknowledge the technical and scientific assistance of Sydney Microscopy and Microanalysis, the University of Sydney node of Microscopy Australia (scanning of slides).  ... 
doi:10.3390/cancers14143441 pmid:35884502 pmcid:PMC9316952 fatcat:rp74vy2grfbwrhavdhaegqeuya

Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

Shivam Kalra, H. R. Tizhoosh, Sultaan Shah, Charles Choi, Savvas Damaskinos, Amir Safarpoor, Sobhan Shafiei, Morteza Babaie, Phedias Diamandis, Clinton J. V. Campbell, Liron Pantanowitz
2020 npj Digital Medicine  
Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology.  ...  The emergence of digital pathology has opened new horizons for histopathology.  ...  ACKNOWLEDGEMENTS We would like to thank the Ontario Government for awarding an ORF-RE grant for this project (Ontario Research Fund-Research Excellence).  ... 
doi:10.1038/s41746-020-0238-2 pmid:32195366 pmcid:PMC7064517 fatcat:eerijsx2arhx7hojy3pbc6fj4a

Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence [article]

Shivam Kalra, H.R. Tizhoosh, Sultaan Shah, Charles Choi, Savvas Damaskinos, Amir Safarpoor, Sobhan Shafiei, Morteza Babaie, Phedias Diamandis, Clinton JV Campbell, Liron Pantanowitz
2019 arXiv   pre-print
Whereas machine learning involving classification and segmentation methods have obvious benefits for image analysis in pathology, image search represents a fundamental shift in computational pathology.  ...  The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer  ...  Acknowledgements We would like to thank the Ontario Government for awarding an ORF-RE grant for this project (Ontario Research Fund -Research Excellence).  ... 
arXiv:1911.08736v1 fatcat:nv6d74csnbbwhompra5b5opmde

Computer-Assisted Analysis of Biomedical Images [article]

Leonardo Rundo
2021 arXiv   pre-print
Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications.  ...  As a matter of fact, the proposed computer-assisted bioimage analysis methods can be beneficial for the definition of imaging biomarkers, as well as for quantitative medicine and biology.  ...  [2] proposed a CNN-based architecture for localization, classification, and tracking in 4D fluorescence microscopy imaging.  ... 
arXiv:2106.04381v1 fatcat:osqiyd3sbja3zgrby7bf4eljfm

The Following are Abstracts from the Second International Conference of the European Society for Molecular Imaging in Naples, Italy June 14-15, 2007

2007 Molecular Imaging  
Topics covered in this lecture include-but are not limited to-vascular and lymphatic targeting, molecular-genetic imaging, and other applications of toolkits of scientific and medical value.  ...  Such a set of ligand-receptor interactions can encompass applications in different organ-specific vascular beds in health and diseased conditions.  ...  Scanning near-field optical microscopy (SNOM) is a high-resolution imaging technique that is gaining, in the last few years, a prominent role in cell biology research. 1 SNOM provides simultaneous topographic  ... 
doi:10.2310/7290.2007.00032 fatcat:hl2ynabt3bez7bt6vllibmsy3y

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis.  ...  In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data  ...  Our survey comprehensively analyses cancer imaging GAN applications focusing on radiology modalities.  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he
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