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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1972 1
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1983 1
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1987 1
1989 3
1992 1
1993 2
1994 2
1995 8
1996 6
1997 4
1998 1
1999 3
2000 7
2001 2
2002 2
2004 5
2005 4
2006 3
2007 7
2008 10
2009 12
2010 11
2011 8
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2013 9
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236 results

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Page 1
PENet: Prior evidence deep neural network for bladder cancer staging.
Zhou X, Yue X, Xu Z, Denoeux T, Chen Y. Zhou X, et al. Methods. 2022 Nov;207:20-28. doi: 10.1016/j.ymeth.2022.08.010. Epub 2022 Aug 27. Methods. 2022. PMID: 36031139
Our investigation reveals that the prediction error and the variance of PENet may be reduced by giving the network prior evidence that is consistent with the ground truth. Using MR image datasets, experiments show that PENet performs better than image-based DCNN alg …
Our investigation reveals that the prediction error and the variance of PENet may be reduced by giving the network prior evidence tha …
PENet: Continuous-Valued Pulmonary Edema Severity Prediction On Chest X-ray Using Siamese Convolutional Networks.
Akbar MN, Wang X, Erdogmus D, Dalal S. Akbar MN, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1834-1838. doi: 10.1109/EMBC48229.2022.9871153. Annu Int Conf IEEE Eng Med Biol Soc. 2022. PMID: 36086469
Although deep learning has shown promise in detecting the presence or absence or discrete grades of severity, of such edema, prediction of continuous-valued severity yet remains a challenge. Here, we propose PENet: Siamese convolutional neural networks to assess the contin …
Although deep learning has shown promise in detecting the presence or absence or discrete grades of severity, of such edema, prediction of c …
PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.
Huang SC, Kothari T, Banerjee I, Chute C, Ball RL, Borus N, Huang A, Patel BN, Rajpurkar P, Irvin J, Dunnmon J, Bledsoe J, Shpanskaya K, Dhaliwal A, Zamanian R, Ng AY, Lungren MP. Huang SC, et al. NPJ Digit Med. 2020 Apr 24;3:61. doi: 10.1038/s41746-020-0266-y. eCollection 2020. NPJ Digit Med. 2020. PMID: 32352039 Free PMC article.
In this study, we developed a deep learning model-PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solution for this purpose. ...PENet achieved an AUROC of 0.84 [0.82-0.87] on detecting PE on the hold out internal test set and 0.85 [0.81-0 …
In this study, we developed a deep learning model-PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solutio …
PeNet: A feature excitation learning approach to advertisement click-through rate prediction.
Yin Y, Ochieng ND, Sun J, Bao X, Wang Z. Yin Y, et al. Neural Netw. 2024 Apr;172:106127. doi: 10.1016/j.neunet.2024.106127. Epub 2024 Jan 12. Neural Netw. 2024. PMID: 38232422
Thus, we propose a potential feature excitation learning network (PeNet), which is a neural network model based on feature combination and feature interaction. ...Experimental results on multiple benchmark datasets indicate the PeNet as a general-purpose plug-in has …
Thus, we propose a potential feature excitation learning network (PeNet), which is a neural network model based on feature combinatio …
High cumulative risks of cancer in patients with PTEN hamartoma tumour syndrome.
Bubien V, Bonnet F, Brouste V, Hoppe S, Barouk-Simonet E, David A, Edery P, Bottani A, Layet V, Caron O, Gilbert-Dussardier B, Delnatte C, Dugast C, Fricker JP, Bonneau D, Sevenet N, Longy M, Caux F; French Cowden Disease Network. Bubien V, et al. J Med Genet. 2013 Apr;50(4):255-63. doi: 10.1136/jmedgenet-2012-101339. Epub 2013 Jan 18. J Med Genet. 2013. PMID: 23335809 Free article.
Familial breast cancer and DNA repair genes: Insights into known and novel susceptibility genes from the GENESIS study, and implications for multigene panel testing.
Girard E, Eon-Marchais S, Olaso R, Renault AL, Damiola F, Dondon MG, Barjhoux L, Goidin D, Meyer V, Le Gal D, Beauvallet J, Mebirouk N, Lonjou C, Coignard J, Marcou M, Cavaciuti E, Baulard C, Bihoreau MT, Cohen-Haguenauer O, Leroux D, Penet C, Fert-Ferrer S, Colas C, Frebourg T, Eisinger F, Adenis C, Fajac A, Gladieff L, Tinat J, Floquet A, Chiesa J, Giraud S, Mortemousque I, Soubrier F, Audebert-Bellanger S, Limacher JM, Lasset C, Lejeune-Dumoulin S, Dreyfus H, Bignon YJ, Longy M, Pujol P, Venat-Bouvet L, Bonadona V, Berthet P, Luporsi E, Maugard CM, Noguès C, Delnatte C, Fricker JP, Gesta P, Faivre L, Lortholary A, Buecher B, Caron O, Gauthier-Villars M, Coupier I, Servant N, Boland A, Mazoyer S, Deleuze JF, Stoppa-Lyonnet D, Andrieu N, Lesueur F. Girard E, et al. Int J Cancer. 2019 Apr 15;144(8):1962-1974. doi: 10.1002/ijc.31921. Epub 2018 Nov 13. Int J Cancer. 2019. PMID: 30303537 Free PMC article.
Erratum: Author Correction: PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.
Huang SC, Kothari T, Banerjee I, Chute C, Ball RL, Borus N, Huang A, Patel BN, Rajpurkar P, Irvin J, Dunnmon J, Bledsoe J, Shpanskaya K, Dhaliwal A, Zamanian R, Ng AY, Lungren MP. Huang SC, et al. NPJ Digit Med. 2020 Jul 28;3:102. doi: 10.1038/s41746-020-00310-6. eCollection 2020. NPJ Digit Med. 2020. PMID: 32793812 Free PMC article.
Ovarian Cancer Targeted Theranostics.
Nimmagadda S, Penet MF. Nimmagadda S, et al. Front Oncol. 2020 Jan 21;9:1537. doi: 10.3389/fonc.2019.01537. eCollection 2019. Front Oncol. 2020. PMID: 32039018 Free PMC article. Review.
Global disparities in SARS-CoV-2 genomic surveillance.
Brito AF, Semenova E, Dudas G, Hassler GW, Kalinich CC, Kraemer MUG, Ho J, Tegally H, Githinji G, Agoti CN, Matkin LE, Whittaker C; Bulgarian SARS-CoV-2 sequencing group; Communicable Diseases Genomics Network (Australia and New Zealand); COVID-19 Impact Project; Danish Covid-19 Genome Consortium; Fiocruz COVID-19 Genomic Surveillance Network; GISAID core curation team; Network for Genomic Surveillance in South Africa (NGS-SA); Swiss SARS-CoV-2 Sequencing Consortium; Howden BP, Sintchenko V, Zuckerman NS, Mor O, Blankenship HM, de Oliveira T, Lin RTP, Siqueira MM, Resende PC, Vasconcelos ATR, Spilki FR, Aguiar RS, Alexiev I, Ivanov IN, Philipova I, Carrington CVF, Sahadeo NSD, Branda B, Gurry C, Maurer-Stroh S, Naidoo D, von Eije KJ, Perkins MD, van Kerkhove M, Hill SC, Sabino EC, Pybus OG, Dye C, Bhatt S, Flaxman S, Suchard MA, Grubaugh ND, Baele G, Faria NR. Brito AF, et al. Nat Commun. 2022 Nov 16;13(1):7003. doi: 10.1038/s41467-022-33713-y. Nat Commun. 2022. PMID: 36385137 Free PMC article.
Using lightweight convolutional neural network to identify ventilation/perfusion scintigraphy for acute pulmonary embolism.
Ye W, Yang J, Li X, Chen X, Zou F, Gu W. Ye W, et al. Heart Lung. 2025 Nov-Dec;74:198-205. doi: 10.1016/j.hrtlng.2025.07.012. Epub 2025 Jul 23. Heart Lung. 2025. PMID: 40706132
CONCLUSIONS: The accuracy of PENet reached 87.47 %. DA% calculated automatically could reflect PE severity and correlate well with clinical data. PENet shows promising results for clinical use....
CONCLUSIONS: The accuracy of PENet reached 87.47 %. DA% calculated automatically could reflect PE severity and correlate well with cl …
236 results