{"id":2528,"date":"2025-10-10T09:28:28","date_gmt":"2025-10-10T07:28:28","guid":{"rendered":"https:\/\/portale2.unime.it\/ai-healthlab\/?page_id=2528"},"modified":"2025-12-04T21:01:14","modified_gmt":"2025-12-04T20:01:14","slug":"publication","status":"publish","type":"page","link":"https:\/\/portale2.unime.it\/ai-healthlab\/en\/publication\/","title":{"rendered":"PUBLICATIONS"},"content":{"rendered":"\t\t
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OUR PUBLICATIONS

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The joy of discovery is certainly the liveliest that the mind of man can ever feel<\/span><\/h1><\/h4>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Journals<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tJournal<\/u><\/strong><\/span><\/span>\n
\n\u2013 Puglisi L., Alexander D.C., Rav\u00ec D.<\/strong>, et al. \u201cBrain latent progression: Individual-based spatiotemporal disease progression on 3D brain MRIs via latent diffusion\u201d<\/em>. Medical Image Analysis (2025), p. 103734.\n

\n\u2013 Rav\u00ec D.<\/strong>, Barkhof F., Alexander D.C., Puglisi L., Parker G.J., Eshaghi A., et al. \u201cAn efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training\u201d<\/em>. Medical Image Analysis 91 (2024), p. 103033.\n

\n\u2013 Rav\u00ec D.<\/strong>, Blumberg S.B., Ingala S., Barkhof F., Alexander D.C., Oxtoby N.P., et al. \u201cDegenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia\u201d<\/em>. Medical Image Analysis 75 (2022), p. 102257.\n

\n\u2013 Mengoudi K., Rav\u00ec D.<\/strong>, Yong K.X., Primativo S., Pavisic I., Brotherhood E., et al. \u201cAugmenting Dementia Cognitive Assessment with Instruction-less Eye-tracking Tests\u201d<\/em>. IEEE Journal of Biomedical and Health Informatics (2020).\n

\n\u2013 Szczotka A.B., Shakir D.I., Rav\u00ec D.<\/strong>, Clarkson M.J., Pereira S.P., et al. \u201cLearning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches\u201d<\/em>. Intl. J. of Computer Assisted Radiology and Surgery 15.7 (2020), pp. 1167\u20131175.\n

\n\u2013 Fabelo H., Ortega S., Szolna A., Bulters D., Pi\u00f1eiro J.F., Kabwama S., et al. \u201cIn-vivo hyperspectral human brain image database for brain cancer detection\u201d<\/em>. IEEE Access 7 (2019), pp. 39098\u201339116.\n

\n\u2013 Rav\u00ec D.<\/strong>, Szczotka A.B., Pereira S.P., Vercauteren T. \u201cAdversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy\u201d<\/em>. Medical Image Analysis 53 (2019), pp. 123\u2013131.\n

\n\u2013 Fabelo H., Ortega S., Lazcano R., Madro\u00f1al D., Callic\u00f3 G.M., Ju\u00e1rez E., et al. \u201cAn intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation\u201d<\/em>. Sensors 18.2 (2018), p. 430.\n

\n\u2013 Fabelo H., Ortega S., Rav\u00ec D.<\/strong>, et al. \u201cSpatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations\u201d<\/em>. PloS One 13.3 (2018), e0193721.\n

\n\u2013 Rav\u00ec D.<\/strong>, Szczotka A.B., Shakir D.I., Pereira S.P., Vercauteren T. \u201cEffective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction\u201d<\/em>. Intl J Comp Assist Radiol Surg (2018).\n

\n\u2013 Rav\u00ec D.<\/strong>, Fabelo H., Callic\u00f3 G.M., Yang G.-Z. \u201cManifold embedding and semantic segmentation for intraoperative guidance with hyperspectral brain imaging\u201d<\/em>. IEEE Transactions on Medical Imaging 36.9 (2017), pp. 1845\u20131857.\n

\n\u2013 Kabwama S., Bulters D., Bulstrode H., Fabelo H., Ortega S., Callic\u00f3 G., Stanciulescu B., Kiran R., Rav\u00ec D.<\/strong>, Szolna A., et al. \u201cIntra-operative Hyperspectral Imaging for Brain Tumour Detection and Delineation: Current Progress on the HELICOID Project (0018)\u201d<\/em>. International Journal of Surgery 36 (2016), S140.\n

\n\u2013 Rav\u00ec D.<\/strong>, Bober M., Farinella G.M., Guarnera M., Battiato S. \u201cSemantic segmentation of images exploiting DCT-based features and random forest\u201d<\/em>. Pattern Recognition 52 (2016), pp. 260\u2013273.\n

\n\u2013 Rav\u00ec D.<\/strong>, Wong C., Lo B., Yang G.-Z. \u201cA deep learning approach to on-node sensor data analytics for mobile or wearable devices\u201d<\/em>. IEEE J Biomed Health Informatics 21.1 (2016), pp. 56\u201364.\n

\n\u2013 Rav\u00ec D.<\/strong>, Wong C., Deligianni F., Berthelot M., Andreu-Perez J., Lo B., Yang G.-Z. \u201cDeep learning for health informatics\u201d<\/em>. IEEE J Biomed Health Informatics 21.1 (2016), pp. 4\u201321.\n

\n\u2013 Farinella G.M., Rav\u00ec D.<\/strong>, Tomaselli V., Guarnera M., Battiato S. \u201cRepresenting scenes for real-time context classification on mobile devices\u201d<\/em>. Pattern Recognition 48.4 (2015), pp. 1086\u20131100.\n

\n\u2013 Battiato S., Farinella G.M., Puglisi G., Rav\u00ec D.<\/strong>. \u201cAligning codebooks for near duplicate image detection\u201d<\/em>. Multimedia Tools and Applications 72.2 (2014), pp. 1483\u20131506.\n

\n\u2013 Battiato S., Farinella G.M., Puglisi G., Rav\u00ec D.<\/strong>. \u201cSaliency-based selection of gradient vector flow paths for content aware image resizing\u201d<\/em>. IEEE Transactions on Image Processing 23.5 (2014), pp. 2081\u20132095.\n

\n\u2013 Battiato S., Farinella G.M., Guarnera M., Messina G., Rav\u00ec D.<\/strong>. \u201cRed-eyes removal through cluster-based boosting on gray codes\u201d<\/em>. EURASIP J Image and Video Processing 2010.1 (2010), p. 909043.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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International Conference<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tInternational Conferences<\/u><\/strong><\/span>\n<\/span>\n\n
\n\u2013 McMaster E., Puglisi L., Gao C., Krishnan A.R., Saunders A.M., Rav\u00ec D.<\/strong>, et al. \u201cA technical assessment of latent diffusion for Alzheimer’s disease progression\u201d<\/em>. Medical Imaging 2025: Image Processing, Vol. 13406, SPIE (2025), pp. 505\u2013513.\n\n

\n\u2013 Moschetto A., Puglisi L., Sargood A., Dell\u2019Acqua P., Guarnera F., Battiato S., Rav\u00ec D.<\/strong>. \u201cBenchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translation\u201d<\/em>. Computer Vision and Generative Models for Medical Imaging, ICIAP (2025).\n\n

\n\u2013 Slator P.J., Blumberg S.B., Lin H., Slumbers O., Rav\u00ec D.<\/strong>, Zhou Y., et al. \u201cDeep Learning Experimental Design for Quantitative Parameter Mapping\u201d<\/em>. International Society for Magnetic Resonance in Medicine (2025).\n\n

\n\u2013 Puglisi L., Alexander D.C., Rav\u00ec D.<\/strong>. \u201cEnhancing spatiotemporal disease progression models via latent diffusion and prior knowledge\u201d<\/em>. MICCAI (2024), Springer Nature Switzerland, pp. 173\u2013183.\n\n

\n\u2013 Puglisi L., Rondinella A., De Meo L., Guarnera F., Battiato S., Rav\u00ec D.<\/strong>. \u201cSynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions\u201d<\/em>. IEEE MetroXRAINE (2024), pp. 559\u2013564.\n\n

\n\u2013 Litrico M., Guarnera F., Giuffrida M.V., Rav\u00ec D.<\/strong>, Battiato S. \u201cTADM: Temporally-aware diffusion model for neurodegenerative progression on brain MRI\u201d<\/em>. MICCAI (2024), Springer Nature, pp. 444\u2013453.\n\n

\n\u2013 Poland D.J., Puglisi L., Rav\u00ec D.<\/strong>. \u201cIndustrial Machines Health Prognosis Using a Transformer-Based Framework\u201d<\/em>. IEEE MetroXRAINE (2024), pp. 776\u2013781.\n\n

\n\u2013 Raciti R., Rondinella A., Puglisi L., Guarnera F., Rav\u00ec D.<\/strong>, Battiato S. \u201cEfficient Atrophy Mapping: A Single-Step U-Net Approach for Rapid Brain Change Estimation\u201d<\/em>. IEEE MetroXRAINE (2024), pp. 553\u2013558.\n

\n\n\u2013 Trenta F., Battiato S., Rav\u00ec D.<\/strong>. \u201cAn explainable medical imaging framework for modality classifications trained using small datasets\u201d<\/em>. ICIAP (2022), Springer, pp. 358\u2013367.\n\n

\n\u2013 Mengoudi K., Rav\u00ec D.<\/strong>, Yong K.X., Primativo S., Pavisic I.M., et al. \u201cAugmenting cognitive assessment with instruction-less Eye-tracking tests: A machine learning approach for detecting abnormal oculomotor biomarkers\u201d<\/em>. Alzheimer\u2019s & Dementia 16 (2020), e045318.\n\n

\n\u2013 Rav\u00ec D.<\/strong>, Ghavami N., Alexander D.C., Ianus A. \u201cCurrent applications and future promises of machine learning in diffusion MRI\u201d<\/em>. MICCAI (2019), Springer, pp. 105\u2013121.\n\n

\n\u2013 Rav\u00ec D.<\/strong>, Alexander D.C., Oxtoby N.P., Initiative A.D.N. \u201cDegenerative adversarial neuroimage nets: Generating images that mimic disease progression\u201d<\/em>. MICCAI (2019), Springer, pp. 164\u2013172.\n\n

\n\u2013 Zhang R., Rav\u00ec D.<\/strong>, Yang G.-Z., Lo B. \u201cA personalized air quality sensing system\u2014a preliminary study on assessing the air quality of London underground stations\u201d<\/em>. IEEE BSN (2017), pp. 111\u2013114.\n\n

\n\u2013 Rav\u00ec D.<\/strong>, Wong C., Lo B., Yang G.-Z. \u201cDeep learning for human activity recognition: A resource efficient implementation on low-power devices\u201d<\/em>. IEEE BSN (2016), pp. 71\u201376.\n

\n\n\u2013 Rav\u00ec D.<\/strong>, Lo B., Yang G.-Z. \u201cReal-Time Food Intake Classification and Energy Expenditure Estimation on a Mobile Device\u201d<\/em>. Body Sensor Network (2015).\n\n

\n\u2013 Battiato S., Farinella G.M., Guarnera M., Rav\u00ec D.<\/strong>, Tomaselli V. \u201cInstant scene recognition on mobile platform\u201d<\/em>. ECCV (2012), Springer, pp. 655\u2013658.\n\n

\n\u2013 Battiato S., Farinella G.M., Puglisi G., Rav\u00ec D.<\/strong>. \u201cContent-aware image resizing with seam selection based on gradient vector flow\u201d<\/em>. IEEE ICIP (2012), pp. 2117\u20132120.\n\n

\n\u2013 Battiato S., Farinella G.M., Guarnera M., Messina G., Rav\u00ec D.<\/strong>. \u201cRed-eyes removal through cluster based Linear Discriminant Analysis\u201d<\/em>. IEEE ICIP (2010), pp. 2185\u20132188.\n

\n\n\u2013 Battiato S., Farinella G.M., Guarnera M., Messina G., Rav\u00ec D.<\/strong>. \u201cBoosting gray codes for red eyes removal\u201d<\/em>. ICPR (2010), pp. 4214\u20134217.\n

\n\n\u2013 Battiato S., Farinella G.M., Gallo G., Rav\u00ec D.<\/strong>. \u201cSpatial hierarchy of textons distributions for scene classification\u201d<\/em>. Intl. Conf. on Multimedia Modeling (2009), pp. 333\u2013343.\n

\n\n\u2013 Battiato S., Farinella G.M., Gallo G., Rav\u00ec D.<\/strong>. \u201cScene categorization using bag of textons on spatial hierarchy\u201d<\/em>. IEEE ICIP (2008), pp. 2536\u20132539.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Other<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tPatent<\/u><\/strong><\/span>\n<\/span>\n\n

\u2013 Messina G., Rav\u00ec D.<\/strong>, Guarnera M., Farinella G.M. \u201cMethod and apparatus for filtering red and\/or golden eye artifacts\u201d<\/em>. U.S. Patent 8,498,496. July 2013.<\/p>\n
\nWhite Paper<\/u><\/strong><\/span>\n<\/span>\n\n

\u2013 Perez J.A., Deligianni F., Rav\u00ec D.<\/strong>, Yang G.-Z., et al. \u201cArtificial intelligence and robotics\u201d<\/em>. (2018).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

OUR PUBLICATIONS The joy of discovery is certainly the liveliest that the mind of man can ever feel Journals Journal \u2013 Puglisi L., Alexander D.C., Rav\u00ec D., et al. \u201cBrain latent progression: Individual-based spatiotemporal disease progression on 3D brain MRIs via latent diffusion\u201d. Medical Image Analysis (2025), p. 103734. \u2013 Rav\u00ec D., Barkhof F., Alexander… Continua a leggere PUBLICATIONS<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":""},"class_list":["post-2528","page","type-page","status-publish","hentry","entry"],"yoast_head":"\nPUBLICATIONS - AI-HealthLab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/portale2.unime.it\/ai-healthlab\/en\/publication\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PUBLICATIONS - AI-HealthLab\" \/>\n<meta property=\"og:description\" content=\"OUR PUBLICATIONS The joy of discovery is certainly the liveliest that the mind of man can ever feel Journals Journal \u2013 Puglisi L., Alexander D.C., Rav\u00ec D., et al. \u201cBrain latent progression: Individual-based spatiotemporal disease progression on 3D brain MRIs via latent diffusion\u201d. Medical Image Analysis (2025), p. 103734. \u2013 Rav\u00ec D., Barkhof F., Alexander… Continua a leggere PUBLICATIONS\" \/>\n<meta property=\"og:url\" content=\"https:\/\/portale2.unime.it\/ai-healthlab\/en\/publication\/\" \/>\n<meta property=\"og:site_name\" content=\"AI-HealthLab\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-04T20:01:14+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/portale2.unime.it\/ai-healthlab\/wp-content\/uploads\/sites\/94\/2025\/10\/0x0.jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Tempo di lettura stimato\" \/>\n\t<meta name=\"twitter:data1\" content=\"7 minuti\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/\",\"url\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/\",\"name\":\"PUBLICATIONS - AI-HealthLab\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/wp-content\\\/uploads\\\/sites\\\/94\\\/2025\\\/10\\\/0x0.jpeg\",\"datePublished\":\"2025-10-10T07:28:28+00:00\",\"dateModified\":\"2025-12-04T20:01:14+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/#breadcrumb\"},\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"it-IT\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/#primaryimage\",\"url\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/wp-content\\\/uploads\\\/sites\\\/94\\\/2025\\\/10\\\/0x0.jpeg\",\"contentUrl\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/wp-content\\\/uploads\\\/sites\\\/94\\\/2025\\\/10\\\/0x0.jpeg\",\"width\":1200,\"height\":800},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/en\\\/publication\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"PUBLICATIONS\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#website\",\"url\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/\",\"name\":\"AI-HealthLab\",\"description\":\"From AI to Care: Innovation for Health\",\"publisher\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"it-IT\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#organization\",\"name\":\"AI-HealthLab\",\"url\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"it-IT\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/wp-content\\\/uploads\\\/sites\\\/94\\\/2025\\\/10\\\/generated-image-9.png\",\"contentUrl\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/wp-content\\\/uploads\\\/sites\\\/94\\\/2025\\\/10\\\/generated-image-9.png\",\"width\":1024,\"height\":1024,\"caption\":\"AI-HealthLab\"},\"image\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"PUBLICATIONS - AI-HealthLab","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/portale2.unime.it\/ai-healthlab\/en\/publication\/","og_locale":"it_IT","og_type":"article","og_title":"PUBLICATIONS - AI-HealthLab","og_description":"OUR PUBLICATIONS The joy of discovery is certainly the liveliest that the mind of man can ever feel Journals Journal \u2013 Puglisi L., Alexander D.C., Rav\u00ec D., et al. \u201cBrain latent progression: Individual-based spatiotemporal disease progression on 3D brain MRIs via latent diffusion\u201d. 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