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bib:viper_journal [2023/04/07 17:38] (current)
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-   ​@Article{roman:EAAI2019,+   @article{marini:​color2023,​ 
 +title = {Data-driven color augmentation for H\&E stained images in computational pathology},​ 
 +journal = {Journal of Pathology Informatics},​ 
 +volume = {14}, 
 +pages = {100183}, 
 +year = {2023}, 
 +issn = {2153-3539},​ 
 +doi = {https://​doi.org/​10.1016/​j.jpi.2022.100183},​ 
 +url = {https://​www.sciencedirect.com/​science/​article/​pii/​S2153353922007830},​ 
 +author = {Niccolo Marini and Sebastian Otalora and Marek Wodzinski and Selene Tomassini and Aldo Franco Dragoni and Stephane Marchand-Maillet and Juan Pedro Dominguez Morales and Lourdes Duran-Lopez and Simona Vatrano and Henning Muller and Manfredo Atzori}, 
 +
 + 
 + 
 +   ​@article{Orel:​plos2022,​ 
 +    doi = {10.1371/​journal.pone.0264429},​ 
 +    author = {Orel, Erol AND Esra, Rachel AND Estill, Janne AND Thiabaud, Amaury AND Marchand-Maillet,​ Stéphane AND Merzouki, Aziza AND Keiser, Olivia}, 
 +    journal = {PLOS ONE}, 
 +    publisher = {Public Library of Science}, 
 +    title = {Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa}, 
 +    year = {2022}, 
 +    month = {03}, 
 +    volume = {17}, 
 +    url = {https://​doi.org/​10.1371/​journal.pone.0264429},​ 
 +    pages = {1-15}, 
 +    abstract = {Introduction High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics. ​  ​Methods We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual’s HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies. ​  ​Results Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer’s index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV.   ​Conclusions We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints.},​ 
 +    number = {3}, 
 + 
 +
 + 
 + 
 +   ​@article{bruneau:​remotesensing2021,​ 
 +  author ​   = {Pierrick Bruneau and 
 +               ​Etienne Brangbour and 
 +               ​Stephane Marchand-Maillet and 
 +               ​Renaud Hostache and 
 +               Marco Chini and 
 +               ​Ramona Pelich and 
 +               ​Patrick Matgen and 
 +               ​Thomas Tamisier},​ 
 +  title     = {Measuring the Impact of Natural Hazards with Citizen Science: The 
 +               Case of Flooded Area Estimation Using Twitter}, 
 +  journal ​  = {Remote. Sens.}, 
 +  volume ​   = {13}, 
 +  number ​   = {6}, 
 +  pages     = {1153}, 
 +  year      = {2021}, 
 +  url       = {https://​doi.org/​10.3390/​rs13061153} 
 +  } 
 + 
 + 
 +    @article{ouvrard:​mtap2021,​ 
 +  author ​   = {Xavier Ouvrard and 
 +               ​Jean-Marie Le Goff and 
 +               ​Stephane Marchand-Maillet},​ 
 +  title     = {Exchange-based diffusion in Hb-Graphs: Highlighting complex relationships in multimedia collections},​ 
 +  journal ​  = {Multim. Tools Appl.}, 
 +  volume ​   = {80}, 
 +  number ​   = {15}, 
 +  pages     = {22429--22464},​ 
 +  year      = {2021}, 
 +  url       = {https://​doi.org/​10.1007/​s11042-020-09176-y},​ 
 +  doi       = {10.1007/​s11042-020-09176-y} 
 +
 + 
 + 
 + 
 +    @article{rudinac:​mtap2021,​ 
 +  author ​   = {Stevan Rudinac and 
 +               Jenny Benois-Pineau and 
 +               ​Stephane Marchand-Maillet},​ 
 +  title     = {Special issue on content-based multimedia indexing in the era of artificial 
 +               ​intelligence},​ 
 +  journal ​  = {Multim. Tools Appl.}, 
 +  volume ​   = {80}, 
 +  number ​   = {15}, 
 +  pages     = {23133--23134},​ 
 +  year      = {2021}, 
 +  url       = {https://​doi.org/​10.1007/​s11042-021-10923-y},​ 
 +  doi       = {10.1007/​s11042-021-10923-y},​ 
 +  timestamp = {Thu, 29 Jul 2021 13:41:13 +0200}, 
 +  biburl ​   = {https://​dblp.org/​rec/​journals/​mta/​RudinacBM21.bib},​ 
 +  bibsource = {dblp computer science bibliography,​ https://​dblp.org} 
 +
 + 
 + 
 + 
 +      @article{messina:​acmtransmcca2021,​ 
 +  author ​   = {Nicola Messina and 
 +               ​Giuseppe Amato and 
 +               ​Andrea Esuli and 
 +               ​Fabrizio Falchi and 
 +               ​Claudio Gennaro and 
 +               ​St{\'​{e}}phane Marchand{-}Maillet},​ 
 +  title     = {Fine-Grained Visual Textual Alignment for Cross-Modal Retrieval Using 
 +               ​Transformer Encoders},​ 
 +  journal ​  = {{ACM} Trans. Multim. Comput. Commun. Appl.}, 
 +  volume ​   = {17}, 
 +  number ​   = {4}, 
 +  pages     = {128:​1--128:​23},​ 
 +  year      = {2021}, 
 +  url       = {https://​doi.org/​10.1145/​3451390},​ 
 +  doi       = {10.1145/​3451390} 
 +
 + 
 + 
 +   ​@article{orel:​medrxiv2020,​ 
 + author = {Orel, Erol and Esra, Rachel and Estill, Janne and Marchand-Maillet,​ St{\'​e}phane and Merzouki, Aziza and Keiser, Olivia}, 
 + title = {Machine learning to identify socio-behavioural predictors of HIV positivity in East and Southern Africa}, 
 + elocation-id = {2020.01.27.20018242},​ 
 + year = {2020}, 
 + doi = {10.1101/​2020.01.27.20018242},​ 
 + publisher = {Cold Spring Harbor Laboratory Press}, 
 + URL = {https://​www.medrxiv.org/​content/​early/​2020/​01/​27/​2020.01.27.20018242},​ 
 + eprint = {https://​www.medrxiv.org/​content/​early/​2020/​01/​27/​2020.01.27.20018242.full.pdf},​ 
 + journal = {medRxiv} 
 +
 + 
 + 
 +   ​@Article{silva:​IS2021,​ 
 +  author ​   = {Yasin Silva and Stephane Marchand-Maillet},​ 
 +  title     = {Introduction to Special Issue of the 11th International Conference on Similarity Search and Applications (SISAP 2017)}, 
 +  journal ​  = {Informations Systems}, 
 +  volume ​   = {95}, 
 +  year      = {2021} 
 +
 + 
 + 
 +   ​@article{graziani:​cbm2020,​ 
 +  author ​   = {Mara Graziani and Vincent Andrearczyk and St{\'​{e}}phane Marchand{-}Maillet and Henning M{\"​{u}}ller},​ 
 +  title     = {Concept attribution:​ Explaining {CNN} decisions to physicians},​ 
 +  journal ​  = {Computers in Biology and Medicine},​ 
 +  volume ​   = {123}, 
 +  pages     = {103865}, 
 +  year      = {2020}, 
 +  url       = {https://​doi.org/​10.1016/​j.compbiomed.2020.103865} 
 +
 + 
 + 
 +   ​@article{ouvrard:​endm2018,​ 
 +  author ​   = {Xavier Ouvrard and 
 +               ​Jean-Marie Le Goff and 
 +               ​Stephane Marchand-Maillet},​ 
 +  title     = {On Adjacency and e-Adjacency in General Hypergraphs:​ Towards a New e-Adjacency Tensor}, 
 +  journal ​  = {Electronic Notes in Discrete Mathematics},​ 
 +  volume ​   = {70}, 
 +  pages     = {71--76}, 
 +  year      = {2018}, 
 +  url       = {https://​doi.org/​10.1016/​j.endm.2018.11.012} 
 +
 + 
 + 
 + 
 +   ​@article{ouvrard:​endm2018,​ 
 +  author ​   = {Xavier Ouvrard and 
 +               ​Jean-Marie Le Goff and 
 +               ​Stephane Marchand-Maillet},​ 
 +  title     = {On Adjacency and e-Adjacency in General Hypergraphs:​ Towards a New e-Adjacency Tensor}, 
 +  journal ​  = {Electronic Notes in Discrete Mathematics},​ 
 +  volume ​   = {70}, 
 +  pages     = {71--76}, 
 +  year      = {2018}, 
 +  url       = {https://​doi.org/​10.1016/​j.endm.2018.11.012} 
 +
 + 
 + 
 + 
 +   @Article{amsaleg:IS2020,
   author ​   = {Laurent Amsaleg and Stephane Marchand-Maillet},​   author ​   = {Laurent Amsaleg and Stephane Marchand-Maillet},​
   title     = {Introduction to Special Issue of the 10th International Conference on Similarity Search and Applications (SISAP 2017)},   title     = {Introduction to Special Issue of the 10th International Conference on Similarity Search and Applications (SISAP 2017)},
bib/viper_journal.1586278471.txt.gz · Last modified: 2020/04/07 18:54 by marchand
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Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion