A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Spectral Machine Learning for Pancreatic Mass Imaging Classification
[article]
2021
arXiv
pre-print
Preserved Fulltext
We present a novel spectral machine learning (SML) method in screening for pancreatic mass using CT imaging. ...
state-of-the-art machine learning classification, gradient boosting and random forest. ...
We present a novel spectral machine learning (SML) method for the diagnosis of pancreatic masses on CT scan. 1) Specifically, for each patient, it extracts the first two spike eigenvectors of the covariance ...
arXiv:2105.00728v1
fatcat:kyz4sn3kc5e2xmvrcqvaitcaaq
Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance
2020
American journal of translational research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. ...
The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. ...
Acknowledgements We would like to thank Ayumi Iizuka, Setsuko Fukuta and Neil Loftus for the help of PESI-MS analysis and Taiwan Pancreas Foundation's support for the international collaborative project ...
pmid:32051746
pmcid:PMC7013221
fatcat:zkhc3xaobvbfdiqtcojqk2k2hm
New advances in CT imaging of pancreas diseases: a narrative review
2020
Gland surgery
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis ...
Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided. ...
The typical tasks the machine learning algorithms are trained for include the segmentation, detection or classification of tumor lesions (17, 48, 65) . ...
doi:10.21037/gs-20-551
pmid:33447580
pmcid:PMC7804533
fatcat:qnlnd3qmcjadnkuyopoijqg7ja
Pancreatic cancer detection and characterization—state of the art cross-sectional imaging and imaging data analysis
2019
Translational Gastroenterology and Hepatology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
State of the art radiologic high-resolution cross-sectional imaging by computed tomography (CT) and magnetic resonance imaging (MRI) represent advanced techniques for early lesion detection, pre-therapeutic ...
In light of molecular taxonomies currently under development, the implementation of advanced imaging data post-processing pipelines and the integration of imaging and clinical data for the development ...
for Selection, Monitoring and Individualization of Cancer Therapies" (SFB824, project C6). ...
doi:10.21037/tgh.2019.05.04
pmid:31231702
pmcid:PMC6556687
fatcat:3dxxjnax4vhablnwr7ocfwmefa
Front Matter: Volume 10134
2017
Medical Imaging 2017: Computer-Aided Diagnosis
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
features and classification methods for Barrett's cancer detection using VLE imaging 10134 0E Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography 10134 0F Fully ...
images [10134-30]
10134 0X
Quantification of CT images for the classification of high-and low-risk pancreatic cysts
[10134-31]
10134 0Y
Automated liver elasticity calculation for 3D MRE [10134- ...
doi:10.1117/12.2277119
dblp:conf/micad/X17
fatcat:ika7pheqxngdxejyvkss4dkbv4
The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging
2021
Metabolites
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. ...
Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is ...
This applies to all fields of machine learning including image analysis. ...
doi:10.3390/metabo11110752
pmid:34822410
pmcid:PMC8624953
fatcat:g7hhjsat3rbvtay5la2kdlu7am
Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Automated machine learning-based diagnosis of sepsis based on HSI data, however, has not been explored to date. ...
HSI-based tissue classification. ...
The present contribution is further supported by the Helmholtz Association under the joint research school "HIDSS Health -Helmholtz Information and Data Science School for Health" and the Helmholtz Imaging ...
arXiv:2106.08445v1
fatcat:devokwfubvcafk56goag24o5qq
Hyperspectral Imaging Based on Compressive Sensing: Determining Cancer Margins in Human Pancreatic Tissue ex Vivo, a Pilot Study
2021
Open Journal of Medical Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
SVM Analysis of Reflectance Images of Samples Containing
both Healthy and Malignant Pancreatic Tissue
SVM is a kernel-based machine learning ...
Based remains the primary treatment for most solid mass tumors. ...
doi:10.4236/ojmi.2021.114011
fatcat:4d6x2ed6rfcztfwjdx4gus5hwa
Machine Learning Characterization of Cancer Patients-Derived Extracellular Vesicles using Vibrational Spectroscopies
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
a classification accuracy of above 90 percent when reduced to a spectral frequency range of 1800 to 1940 inverse cm and subjected to a 50:50 training: testing split. ...
Our findings demonstrate that basic machine learning algorithms are powerful applied intelligence tools to distinguish the complex vibrational spectra of cancer patient EVs from those of healthy patients ...
The differential methylation patterns and somatic mutations of cfDNA can be used to train machine learning classification algorithms like Deep Learning networks for cancer diagnosis. ...
arXiv:2107.10332v8
fatcat:kbalvtcwazgvheerwectpxbm4a
A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging
2019
European Radiology Experimental
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ...
Acknowledgements The authors wish to thank Irina Heid, Alexander Muckenhuber, Katja Steiger, Hsi-Yu Yen, and Marcus Schwaiger for their contributions to the conception and implementation of the techniques ...
(DOCX 403 kb) Abbreviations ADC: Apparent diffusion coefficient; AUC: Area under the curve; DWI: Diffusion-weighted imaging; ML: Machine learning; MRI: Magnetic resonance imaging; OS: Overall survival; ...
doi:10.1186/s41747-019-0119-0
pmid:31624935
pmcid:PMC6797674
fatcat:e5wrt2suyrgm3opadlo55w7pcq
Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? ...
Finally but not least, are we, life scientists, clinicians, computer scientists and engineers, ready for working together? ...
His research interests include data mining, and machine learning. ...
arXiv:1901.00642v1
fatcat:fak46q7bgzesll6y4h7i6mcysi
Proteomic Analysis (GeLC–MS/MS) of ePFT-Collected Pancreatic Fluid in Chronic Pancreatitis
2012
Journal of Proteome Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Our workflow provided a mass spectrometry-based approach for the further study of the pancreatic fluid proteome which may lead to the discovery potential biomarkers of chronic pancreatitis. ...
Spectral counting and statistical analysis thereof revealed an additional 38 and 77 proteins that were up-or down-regulated, respectively, in the pancreatic fluid from individuals with chronic pancreatitis ...
ExPlain applied an upstream analysis approach, based on the implementation of machine learning and graph topological analysis algorithms, to identify causality key-nodes in the network of pancreatic disease ...
doi:10.1021/pr2011022
pmid:22243521
pmcid:PMC3294251
fatcat:d6y3kf3d7rditn43zg3sqp4tje
Applications of Raman spectroscopy in cancer diagnosis
2018
Cancer Metastasis Review
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
detection of brain, ovarian, breast, prostate, and pancreatic cancers and circulating tumor cells. ...
This review provides an overview of the theory of Raman spectroscopy, instrumentation used for measurement, and variation of Raman spectroscopic techniques for clinical applications in cancer, including ...
Funding for the Wayne State University projects outlined in this paper were provided by the Paul Strauss Endowed Chair, Henry Ford Health Systems, and Wayne State University SSIM Program. ...
doi:10.1007/s10555-018-9770-9
fatcat:hdznc3biibfjxfod6vsooriboq
Pancreatic Cancer Surgical Resection Margins: Molecular Assessment by Mass Spectrometry Imaging
2016
PLoS Medicine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Ambient mass spectrometry imaging has emerged as a powerful technique for chemical imaging and real-time diagnosis of tissue samples. ...
Surgical resection with microscopically negative margins remains the main curative option for pancreatic cancer; however, in practice intraoperative delineation of resection margins is challenging. ...
Acknowledgments We thank the Stanford Cancer Institute Tissue Procurement Facility for the services provided.
Author Contributions ...
doi:10.1371/journal.pmed.1002108
pmid:27575375
pmcid:PMC5019340
fatcat:e5hj7zuyprbflg46uuy3vo7ahy
Quantitative phase microscopy spatial signatures of cancer cells
2017
Cytometry Part A
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1904.00997v1 fatcat:2p5gldlxgfaurastezvkzkz27u
Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81%-93% sensitivity and ...
The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers. ...
We thank Ruth Gottlieb for significant biological support and advising, Dr. Ksawery Kalinowski for useful technical discussions, and Dr. Itay Barnea for useful biological discussions. *** *** *** *** ...
doi:10.1002/cyto.a.23100
pmid:28426133
fatcat:af7webxqpvc6xaqfhu6japdsoy
« Previous
Showing results 1 — 15 out of 776 results