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50 results on '"Walsh, Sean"'

1. Algorithmic Randomness, Effective Disintegrations, and Rates of Convergence to the Truth

Author

Huttegger, Simon M., Walsh, Sean, and Blando, Francesca Zaffora

Subjects
Mathematics - Logic, Mathematics - Probability, 03D32, 03A10, 03D78, 03F60, 60A10, 60B05, 60G48
Abstract

L\'evy's Upward Theorem says that the conditional expectation of an integrable random variable converges with probability one to its true value with increasing information. In this paper, we use methods from effective probability theory to characterise the probability one set along which convergence to the truth occurs, and the rate at which the convergence occurs. We work within the setting of computable probability measures defined on computable Polish spaces and introduce a new general theory of effective disintegrations. We use this machinery to prove our main results, which (1) identify the points along which certain classes of effective random variables converge to the truth in terms of certain classes of algorithmically random points, and which further (2) identify when computable rates of convergence exist. Our convergence results significantly generalize earlier results within a unifying novel abstract framework, and there are no precursors of our results on computable rates of convergence. Finally, we make a case for the importance of our work for the foundations of Bayesian probability theory., Comment: A few typos in intro section fixed

Published
2024

4. Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

Author

Nan, Yang, Del Ser, Javier, Walsh, Simon, Schönlieb, Carola, Roberts, Michael, Selby, Ian, Howard, Kit, Owen, John, Neville, Jon, Guiot, Julien, Ernst, Benoit, Pastor, Ana, Alberich-Bayarri, Angel, Menzel, Marion I., Walsh, Sean, Vos, Wim, Flerin, Nina, Charbonnier, Jean-Paul, van Rikxoort, Eva, Chatterjee, Avishek, Woodruff, Henry, Lambin, Philippe, Cerdá-Alberich, Leonor, Martí-Bonmatí, Luis, Herrera, Francisco, and Yang, Guang

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research., Comment: 54 pages, 14 figures, accepted by the Information Fusion journal

Published
2022

14. Baduanjin Mind-Body Exercise for Cancer-Related Fatigue: Protocol for a Remotely Delivered Randomized Wait-List Controlled Feasibility Study

Author

Walsh, Sean, primary, Wang, Kang, additional, Lam, Anderson, additional, Du, Shihao, additional, Hu, Yanbin, additional, Sun, Yu-Ting, additional, Tcharkhedian, Elise, additional, Nikas, Evangeline, additional, Webb, Gregory, additional, Moylan, Eugene, additional, Della-Fiorentina, Stephen, additional, Fahey, Paul, additional, Shelley Wang, Xin, additional, Chen, Ming, additional, and Zhu, Xiaoshu, additional

Published
2024
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18. Cell type-specific connectome predicts distributed working memory activity in the mouse brain.

Author

Xingyu Ding, Froudist-Walsh, Sean, Jaramillo, Jorge, Junjie Jiang, and Xiao-Jing Wang

Subjects
*SHORT-term memory, *COGNITIVE ability, *INTERNEURONS, *MICE, *INFORMATION processing, *COGNITION, *LABORATORY mice
Abstract

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Additional file 1 of Deep learning based identification of bone scintigraphies containing metastatic bone disease foci

Author

Ibrahim, Abdalla, Vaidyanathan, Akshayaa, Primakov, Sergey, Belmans, Flore, Bottari, Fabio, Refaee, Turkey, Lovinfosse, Pierre, Jadoul, Alexandre, Derwael, Celine, Hertel, Fabian, Woodruff, Henry C., Zacho, Helle D., Walsh, Sean, Vos, Wim, Occhipinti, Mariaelena, Hanin, François-Xavier, Lambin, Philippe, Mottaghy, Felix M., and Hustinx, Roland

Abstract

Additional file 1.

Published
2023
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28. The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave

Author

Canivet, Perrine, primary, Desir, Colin, additional, Thys, Marie, additional, Henket, Monique, additional, Frix, Anne-Noëlle, additional, Ernst, Benoit, additional, Walsh, Sean, additional, Occhipinti, Mariaelena, additional, Vos, Wim, additional, Maes, Nathalie, additional, Canivet, Jean Luc, additional, Louis, Renaud, additional, Meunier, Paul, additional, and Guiot, Julien, additional

Published
2022
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31. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Author

Nan, Yang, primary, Ser, Javier Del, additional, Walsh, Simon, additional, Schönlieb, Carola, additional, Roberts, Michael, additional, Selby, Ian, additional, Howard, Kit, additional, Owen, John, additional, Neville, Jon, additional, Guiot, Julien, additional, Ernst, Benoit, additional, Pastor, Ana, additional, Alberich-Bayarri, Angel, additional, Menzel, Marion I., additional, Walsh, Sean, additional, Vos, Wim, additional, Flerin, Nina, additional, Charbonnier, Jean-Paul, additional, van Rikxoort, Eva, additional, Chatterjee, Avishek, additional, Woodruff, Henry, additional, Lambin, Philippe, additional, Cerdá-Alberich, Leonor, additional, Martí-Bonmatí, Luis, additional, Herrera, Francisco, additional, and Yang, Guang, additional

Published
2022
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33. An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography

Author

Vaidyanathan, Akshayaa, primary, Guiot, Julien, additional, Zerka, Fadila, additional, Belmans, Flore, additional, Van Peufflik, Ingrid, additional, Deprez, Louis, additional, Danthine, Denis, additional, Canivet, Gregory, additional, Lambin, Philippe, additional, Walsh, Sean, additional, Occhipinti, Mariaelena, additional, Meunier, Paul, additional, Vos, Wim, additional, Lovinfosse, Pierre, additional, and Leijenaar, Ralph T.H., additional

Published
2022
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35. A connectome-based model of conscious access in monkey cortex

Author

Klatzmann, Ulysse, primary, Froudist-Walsh, Sean, additional, Bliss, Daniel P., additional, Theodoni, Panagiota, additional, Mejías, Jorge, additional, Niu, Meiqi, additional, Rapan, Lucija, additional, Palomero-Gallagher, Nicola, additional, Sergent, Claire, additional, Dehaene, Stanislas, additional, and Wang, Xiao-Jing, additional

Published
2022
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36. A Nasal Genomic Test To Evaluate Lung Cancer Risk in Patients with Pulmonary Nodules

Author

Lamb, Carla, primary, Rieger-Christ, Kimberly, additional, Reddy, Chakravarthy, additional, Huang, Jing, additional, Ding, Jie, additional, Walsh, Sean, additional, Bulman, William, additional, Lofaro, Lori R., additional, Wahidi, Momen M., additional, Feller-Kopman, David, additional, Spira, Avrum, additional, Kennedy, Giulia, additional, and Mazzone, Peter J., additional

Published
2022
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37. Toward next-generation primate neuroscience: A collaboration-based strategic plan for integrative neuroimaging

Author

Milham, Michael, primary, Petkov, Chris, additional, Belin, Pascal, additional, Ben Hamed, Suliann, additional, Evrard, Henry, additional, Fair, Damien, additional, Fox, Andrew, additional, Froudist-Walsh, Sean, additional, Hayashi, Takuya, additional, Kastner, Sabine, additional, Klink, Chris, additional, Majka, Piotr, additional, Mars, Rogier, additional, Messinger, Adam, additional, Poirier, Colline, additional, Schroeder, Charles, additional, Shmuel, Amir, additional, Silva, Afonso C., additional, Vanduffel, Wim, additional, Van Essen, David C., additional, Wang, Zheng, additional, Roe, Anna Wang, additional, Wilke, Melanie, additional, Xu, Ting, additional, Aarabi, Mohammad Hadi, additional, Adolphs, Ralph, additional, Ahuja, Aarit, additional, Alvand, Ashkan, additional, Amiez, Celine, additional, Autio, Joonas, additional, Azadi, Reza, additional, Baeg, Eunha, additional, Bai, Ruiliang, additional, Bao, Pinglei, additional, Basso, Michele, additional, Behel, Austin K., additional, Bennett, Yvonne, additional, Bernhardt, Boris, additional, Biswal, Bharat, additional, Boopathy, Sethu, additional, Boretius, Susann, additional, Borra, Elena, additional, Boshra, Rober, additional, Buffalo, Elizabeth, additional, Cao, Long, additional, Cavanaugh, James, additional, Celine, Amiez, additional, Chavez, Gianfranco, additional, Chen, Li Min, additional, Chen, Xiaodong, additional, Cheng, Luqi, additional, Chouinard-Decorte, Francois, additional, Clavagnier, Simon, additional, Cléry, Justine, additional, Colcombe, Stan J., additional, Conway, Bevil, additional, Cordeau, Melina, additional, Coulon, Olivier, additional, Cui, Yue, additional, Dadarwal, Rakshit, additional, Dahnke, Robert, additional, Desrochers, Theresa, additional, Deying, Li, additional, Dougherty, Kacie, additional, Doyle, Hannah, additional, Drzewiecki, Carly M., additional, Duyck, Marianne, additional, Arachchi, Wasana Ediri, additional, Elorette, Catherine, additional, Essamlali, Abdelhadi, additional, Evans, Alan, additional, Fajardo, Alfonso, additional, Figueroa, Hector, additional, Franco, Alexandre, additional, Freches, Guilherme, additional, Frey, Steve, additional, Friedrich, Patrick, additional, Fujimoto, Atsushi, additional, Fukunaga, Masaki, additional, Gacoin, Maeva, additional, Gallardo, Guillermo, additional, Gao, Lixia, additional, Gao, Yang, additional, Garside, Danny, additional, Garza-Villarreal, Eduardo A., additional, Gaudet-Trafit, Maxime, additional, Gerbella, Marzio, additional, Giavasis, Steven, additional, Glen, Daniel, additional, Ribeiro Gomes, Ana Rita, additional, Torrecilla, Sandra Gonzalez, additional, Gozzi, Alessandro, additional, Gulli, Roberto, additional, Haber, Suzanne, additional, Hadj-Bouziane, Fadila, additional, Fujimoto, Satoka Hashimoto, additional, Hawrylycz, Michael, additional, He, Quansheng, additional, He, Ye, additional, Heuer, Katja, additional, Hiba, Bassem, additional, Hoffstaedter, Felix, additional, Hong, Seok-Jun, additional, Hori, Yuki, additional, Hou, Yujie, additional, Howard, Amy, additional, de la Iglesia-Vaya, Maria, additional, Ikeda, Takuro, additional, Jankovic-Rapan, Lucija, additional, Jaramillo, Jorge, additional, Jedema, Hank P., additional, Jin, Hecheng, additional, Jiang, Minqing, additional, Jung, Benjamin, additional, Kagan, Igor, additional, Kahn, Itamar, additional, Kiar, Gregory, additional, Kikuchi, Yuki, additional, Kilavik, Bjørg, additional, Kimura, Nobuyuki, additional, Klatzmann, Ulysse, additional, Kwok, Sze Chai, additional, Lai, Hsin-Yi, additional, Lamberton, Franck, additional, Lehman, Julia, additional, Li, Pengcheng, additional, Li, Xinhui, additional, Li, Xinjian, additional, Liang, Zhifeng, additional, Liston, Conor, additional, Little, Roger, additional, Liu, Cirong, additional, Liu, Ning, additional, Liu, Xiaojin, additional, Liu, Xinyu, additional, Lu, Haidong, additional, Loh, Kep Kee, additional, Madan, Christopher, additional, Magrou, Loïc, additional, Margulies, Daniel, additional, Mathilda, Froesel, additional, Mejia, Sheyla, additional, Meng, Yao, additional, Menon, Ravi, additional, Meunier, David, additional, Mitchell, A.J., additional, Mitchell, Anna, additional, Murphy, Aidan, additional, Mvula, Towela, additional, Ortiz-Rios, Michael, additional, Ortuzar Martinez, Diego Emanuel, additional, Pagani, Marco, additional, Palomero-Gallagher, Nicola, additional, Pareek, Vikas, additional, Perkins, Pierce, additional, Ponce, Fernanda, additional, Postans, Mark, additional, Pouget, Pierre, additional, Qian, Meizhen, additional, Ramirez, Julian “Bene”, additional, Raven, Erika, additional, Restrepo, Isabel, additional, Rima, Samy, additional, Rockland, Kathleen, additional, Rodriguez, Nadira Yusif, additional, Roger, Elise, additional, Hortelano, Eduardo Rojas, additional, Rosa, Marcello, additional, Rossi, Andrew, additional, Rudebeck, Peter, additional, Russ, Brian, additional, Sakai, Tomoko, additional, Saleem, Kadharbatcha S., additional, Sallet, Jerome, additional, Sawiak, Stephen, additional, Schaeffer, David, additional, Schwiedrzik, Caspar M., additional, Seidlitz, Jakob, additional, Sein, Julien, additional, Sharma, Jitendra, additional, Shen, Kelly, additional, Sheng, Wei-an, additional, Shi, Neo Sunhang, additional, Shim, Won Mok, additional, Simone, Luciano, additional, Sirmpilatze, Nikoloz, additional, Sivan, Virginie, additional, Song, Xiaowei, additional, Tanenbaum, Aaron, additional, Tasserie, Jordy, additional, Taylor, Paul, additional, Tian, Xiaoguang, additional, Toro, Roberto, additional, Trambaiolli, Lucas, additional, Upright, Nick, additional, Vezoli, Julien, additional, Vickery, Sam, additional, Villalon, Julio, additional, Wang, Xiaojie, additional, Wang, Yufan, additional, Weiss, Alison R., additional, Wilson, Charlie, additional, Wong, Ting-Yat, additional, Woo, Choong-Wan, additional, Wu, Bichan, additional, Xiao, Du, additional, Xu, Augix Guohua, additional, Xu, Dongrong, additional, Xufeng, Zhou, additional, Yacoub, Essa, additional, Ye, Ningrong, additional, Ying, Zhang, additional, Yokoyama, Chihiro, additional, Yu, Xiongjie, additional, Yue, Shasha, additional, Yuheng, Lu, additional, Yumeng, Xin, additional, Zaldivar, Daniel, additional, Zhang, Shaomin, additional, Zhao, Yuguang, additional, and Zuo, Zhanguang, additional

Published
2022
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38. The neural basis of delayed gratification

Author

Gao, Zilong, primary, Wang, Hanqing, additional, Lu, Chen, additional, Lu, Tiezhan, additional, Froudist-Walsh, Sean, additional, Chen, Ming, additional, Wang, Xiao-Jing, additional, Hu, Ji, additional, and Sun, Wenzhi, additional

Published
2021
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42. Enabling an interstitial lung disease genomic classifier on an automated platform

Author

Jiang, Huimin, primary, Schaper, Carl, additional, Qu, Jianghan, additional, Davis, Nicole, additional, Fedorowicz, Grazyna, additional, Naranja, Mark, additional, Lee, Kyongryun, additional, Storhoff, James, additional, Pankratz, Dan, additional, Sopory, Ambika, additional, Bhorade, Sangeeta, additional, Huang, Jing, additional, Walsh, Sean, additional, and Kennedy, Giulia, additional

Published
2021
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43. Privacy preserving distributed learning classifiers – Sequential learning with small sets of data

Author

Zerka, Fadila, primary, Urovi, Visara, additional, Bottari, Fabio, additional, Leijenaar, Ralph T.H., additional, Walsh, Sean, additional, Gabrani-Juma, Hanif, additional, Gueuning, Martin, additional, Vaidyanathan, Akshayaa, additional, Vos, Wim, additional, Occhipinti, Mariaelena, additional, Woodruff, Henry C., additional, Dumontier, Michel, additional, and Lambin, Philippe, additional

Published
2021
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44. A polygenic score indexing a DRD2-related co-expression network is associated with striatal dopamine function.

Author

D'Ambrosio, Enrico, Pergola, Giulio, Pardiñas, Antonio F., Dahoun, Tarik, Veronese, Mattia, Sportelli, Leonardo, Taurisano, Paolo, Griffiths, Kira, Jauhar, Sameer, Rogdaki, Maria, Bloomfield, Michael A. P., Froudist-Walsh, Sean, Bonoldi, Ilaria, Walters, James T. R., Blasi, Giuseppe, Bertolino, Alessandro, and Howes, Oliver D.

Subjects
DOPAMINE receptors, DOPAMINE, GENE regulatory networks, PREFRONTAL cortex, SHORT-term memory, GENETIC variation, GENETIC code
Abstract

The D2 dopamine receptor (D2R) is the primary site of the therapeutic action of antipsychotics and is involved in essential brain functions relevant to schizophrenia, such as attention, memory, motivation, and emotion processing. Moreover, the gene coding for D2R (DRD2) has been associated with schizophrenia at a genome-wide level. Recent studies have shown that a polygenic co-expression index (PCI) predicting the brain-specific expression of a network of genes co-expressed with DRD2 was associated with response to antipsychotics, brain function during working memory in patients with schizophrenia, and with the modulation of prefrontal cortex activity after pharmacological stimulation of D2 receptors. We aimed to investigate the relationship between the DRD2 gene network and in vivo striatal dopaminergic function, which is a phenotype robustly associated with psychosis and schizophrenia. To this aim, a sample of 92 healthy subjects underwent 18F-DOPA PET and was genotyped for genetic variations indexing the co-expression of the DRD2-related genetic network in order to calculate the PCI for each subject. The PCI was significantly associated with whole striatal dopamine synthesis capacity (p = 0.038). Exploratory analyses on the striatal subdivisions revealed a numerically larger effect size of the PCI on dopamine function for the associative striatum, although this was not significantly different than effects in other sub-divisions. These results are in line with a possible relationship between the DRD2-related co-expression network and schizophrenia and extend it by identifying a potential mechanism involving the regulation of dopamine synthesis. Future studies are needed to clarify the molecular mechanisms implicated in this relationship. [ABSTRACT FROM AUTHOR]

Published
2022
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45. Conservation challenges for Victorian Banksias: Workshop May 2020.

Author

Muir, Annette, Heyes, Simon, Morgan, John, Hoebee, Susan, Enright, Neal, Whelan, Robert, Geschke, Andrew, Bennett, Andrew, Walsh, Sean, Weatherly, William, and Milne, Robert

Subjects
PLANT competition, SCIENTIFIC knowledge, DROUGHTS, ANIMAL life cycles, HOT weather conditions, HEATHLANDS
Abstract

The challenges facing I Banksia i species conservation in the south-eastern state of Victoria were the focus of an online workshop in May 2020. Banksias are iconic Australian plants, with a Gondwanan origin. [Extracted from the article]

Published
2022
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46. A review in radiomics: Making personalized medicine a reality via routine imaging.

Author

Guiot, Julien, Vaidyanathan, Akshayaa, Deprez, Louis, Zerka, Fadila, Danthine, Denis, Frix, Anne‐Noelle, Lambin, Philippe, Bottari, Fabio, Tsoutzidis, Nathan, Miraglio, Benjamin, Walsh, Sean, Vos, Wim, Hustinx, Roland, Ferreira, Marta, Lovinfosse, Pierre, and Leijenaar, Ralph T.H.

Subjects
RADIOMICS, COMPUTED tomography, INDIVIDUALIZED medicine, DEEP learning, DECISION support systems
Abstract

Radiomics is the quantitative analysis of standard‐of‑care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand‐crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision‐making, from oncology and respiratory medicine to pharmacological and genotyping studies. [ABSTRACT FROM AUTHOR]

Published
2022
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48. The neural basis of delayed gratification.

Author

Zilong Gao, Hanqing Wang, Chen Lu, Tiezhan Lu, Froudist-Walsh, Sean, Ming Chen, Xiao-Jing Wang, Ji Hu, and Wenzhi Sun

Subjects
*DELAY of gratification, *RAPHE nuclei, *DOPAMINERGIC neurons, *MEDICAL sciences, *REWARD (Psychology), *NEUROSCIENCES, *ANIMAL behavior, *ACTION potentials
Abstract

The article presents a study that explores the neural basis of delayed gratification . It mentions that balancing instant gratification versus delayed but better gratification as important for optimizing survival and reproductive success. It supports the hypothesis that delayed gratification involves real-time deliberation.

Published
2021
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49. Distributed learning for optimal radiomics knowledge

Author

Zerka, Fadila, Lambin, Philippe, Woodruff, Henry, Walsh, Sean, Precision Medicine, and RS: GROW - R2 - Basic and Translational Cancer Biology

Subjects
Distributed learning, Blockchain, Decentralized learning, Data privacy
Abstract

This thesis evaluated the current state of research in the field of radiomics and presented an up-to-date overview of distributed learning applications in health care and their limitations. It also presented a new infrastructure to address the limitations of the existing distributed learning solution. The potential of decentralizing the learning process by enforcing trust, immutability, transparency, and traceability in distributed learning networks via blockchain smart contracts was studied. The results showed that the decentralization of distributed learning networks has the potential to train the models on patient data stored at different health care centres without need to exchange patient data or to trust a third party. Furthermore, it evaluated the potential of distributed learning on small sets of data that can mimic the case of low prevalence diseases and phase I clinical trials, where it is not possible for a single health care provider to hold enough data to train reliable AI models. The results suggest that distributed learning 1) overcomes data sharing limitations for low prevalence diseases and can be the new way to apply AI for such diseases, 2) is capable to train models as good as centrally trained models. Moreover, an infrastructure was deployed to a public blockchain (Ethereum) test network and tested the feasibility of decentralized distributed learning in real world settings. The decentralized nature of the study makes it easy for the public to evaluate the time, cost, and the learning quality, hence reaching transparent learning process.

Published
2022

50. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Author

Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, and Yang G

Abstract

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Published
2022
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