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A comprehensive framework for classification of nuclei in digital microscopy imaging: An application to diffuse gliomas
2011
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
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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
2009
IEEE Reviews in Biomedical Engineering
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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
2020
IEEE journal of biomedical and health informatics
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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
2014
Medical Image Analysis
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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/j.media.2013.10.005
pmid:24231667
fatcat:6luivlhrtrdv5dlbh626ohkps4
Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification
2017
IEEE Transactions on Medical Imaging
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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
2013
PLoS ONE
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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]
2022
arXiv
pre-print
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, 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
2017
Nature Biomedical Engineering
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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]
2018
Lecture Notes in Computer Science
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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
2022
Cancers
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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
2020
npj Digital Medicine
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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]
2019
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
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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]
2021
arXiv
pre-print
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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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