OUR PUBLICATIONS
The joy of discovery is certainly the liveliest that the mind of man can ever feel
– Puglisi L., Alexander D.C., Ravì D., et al. “Brain latent progression: Individual-based spatiotemporal disease progression on 3D brain MRIs via latent diffusion”. Medical Image Analysis (2025), p. 103734.
– Ravì D., Barkhof F., Alexander D.C., Puglisi L., Parker G.J., Eshaghi A., et al. “An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training”. Medical Image Analysis 91 (2024), p. 103033.
– Ravì D., Blumberg S.B., Ingala S., Barkhof F., Alexander D.C., Oxtoby N.P., et al. “Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia”. Medical Image Analysis 75 (2022), p. 102257.
– Mengoudi K., Ravì D., Yong K.X., Primativo S., Pavisic I., Brotherhood E., et al. “Augmenting Dementia Cognitive Assessment with Instruction-less Eye-tracking Tests”. IEEE Journal of Biomedical and Health Informatics (2020).
– Szczotka A.B., Shakir D.I., Ravì D., Clarkson M.J., Pereira S.P., et al. “Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches”. Intl. J. of Computer Assisted Radiology and Surgery 15.7 (2020), pp. 1167–1175.
– Fabelo H., Ortega S., Szolna A., Bulters D., Piñeiro J.F., Kabwama S., et al. “In-vivo hyperspectral human brain image database for brain cancer detection”. IEEE Access 7 (2019), pp. 39098–39116.
– Ravì D., Szczotka A.B., Pereira S.P., Vercauteren T. “Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy”. Medical Image Analysis 53 (2019), pp. 123–131.
– Fabelo H., Ortega S., Lazcano R., Madroñal D., Callicó G.M., Juárez E., et al. “An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation”. Sensors 18.2 (2018), p. 430.
– Fabelo H., Ortega S., Ravì D., et al. “Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations”. PloS One 13.3 (2018), e0193721.
– Ravì D., Szczotka A.B., Shakir D.I., Pereira S.P., Vercauteren T. “Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction”. Intl J Comp Assist Radiol Surg (2018).
– Ravì D., Fabelo H., Callicó G.M., Yang G.-Z. “Manifold embedding and semantic segmentation for intraoperative guidance with hyperspectral brain imaging”. IEEE Transactions on Medical Imaging 36.9 (2017), pp. 1845–1857.
– Kabwama S., Bulters D., Bulstrode H., Fabelo H., Ortega S., Callicó G., Stanciulescu B., Kiran R., Ravì D., Szolna A., et al. “Intra-operative Hyperspectral Imaging for Brain Tumour Detection and Delineation: Current Progress on the HELICOID Project (0018)”. International Journal of Surgery 36 (2016), S140.
– Ravì D., Bober M., Farinella G.M., Guarnera M., Battiato S. “Semantic segmentation of images exploiting DCT-based features and random forest”. Pattern Recognition 52 (2016), pp. 260–273.
– Ravì D., Wong C., Lo B., Yang G.-Z. “A deep learning approach to on-node sensor data analytics for mobile or wearable devices”. IEEE J Biomed Health Informatics 21.1 (2016), pp. 56–64.
– Ravì D., Wong C., Deligianni F., Berthelot M., Andreu-Perez J., Lo B., Yang G.-Z. “Deep learning for health informatics”. IEEE J Biomed Health Informatics 21.1 (2016), pp. 4–21.
– Farinella G.M., Ravì D., Tomaselli V., Guarnera M., Battiato S. “Representing scenes for real-time context classification on mobile devices”. Pattern Recognition 48.4 (2015), pp. 1086–1100.
– Battiato S., Farinella G.M., Puglisi G., Ravì D.. “Aligning codebooks for near duplicate image detection”. Multimedia Tools and Applications 72.2 (2014), pp. 1483–1506.
– Battiato S., Farinella G.M., Puglisi G., Ravì D.. “Saliency-based selection of gradient vector flow paths for content aware image resizing”. IEEE Transactions on Image Processing 23.5 (2014), pp. 2081–2095.
– Battiato S., Farinella G.M., Guarnera M., Messina G., Ravì D.. “Red-eyes removal through cluster-based boosting on gray codes”. EURASIP J Image and Video Processing 2010.1 (2010), p. 909043.
– McMaster E., Puglisi L., Gao C., Krishnan A.R., Saunders A.M., Ravì D., et al. “A technical assessment of latent diffusion for Alzheimer’s disease progression”. Medical Imaging 2025: Image Processing, Vol. 13406, SPIE (2025), pp. 505–513.
– Moschetto A., Puglisi L., Sargood A., Dell’Acqua P., Guarnera F., Battiato S., Ravì D.. “Benchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translation”. Computer Vision and Generative Models for Medical Imaging, ICIAP (2025).
– Slator P.J., Blumberg S.B., Lin H., Slumbers O., Ravì D., Zhou Y., et al. “Deep Learning Experimental Design for Quantitative Parameter Mapping”. International Society for Magnetic Resonance in Medicine (2025).
– Puglisi L., Alexander D.C., Ravì D.. “Enhancing spatiotemporal disease progression models via latent diffusion and prior knowledge”. MICCAI (2024), Springer Nature Switzerland, pp. 173–183.
– Puglisi L., Rondinella A., De Meo L., Guarnera F., Battiato S., Ravì D.. “SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions”. IEEE MetroXRAINE (2024), pp. 559–564.
– Litrico M., Guarnera F., Giuffrida M.V., Ravì D., Battiato S. “TADM: Temporally-aware diffusion model for neurodegenerative progression on brain MRI”. MICCAI (2024), Springer Nature, pp. 444–453.
– Poland D.J., Puglisi L., Ravì D.. “Industrial Machines Health Prognosis Using a Transformer-Based Framework”. IEEE MetroXRAINE (2024), pp. 776–781.
– Raciti R., Rondinella A., Puglisi L., Guarnera F., Ravì D., Battiato S. “Efficient Atrophy Mapping: A Single-Step U-Net Approach for Rapid Brain Change Estimation”. IEEE MetroXRAINE (2024), pp. 553–558.
– Trenta F., Battiato S., Ravì D.. “An explainable medical imaging framework for modality classifications trained using small datasets”. ICIAP (2022), Springer, pp. 358–367.
– Mengoudi K., Ravì D., Yong K.X., Primativo S., Pavisic I.M., et al. “Augmenting cognitive assessment with instruction-less Eye-tracking tests: A machine learning approach for detecting abnormal oculomotor biomarkers”. Alzheimer’s & Dementia 16 (2020), e045318.
– Ravì D., Ghavami N., Alexander D.C., Ianus A. “Current applications and future promises of machine learning in diffusion MRI”. MICCAI (2019), Springer, pp. 105–121.
– Ravì D., Alexander D.C., Oxtoby N.P., Initiative A.D.N. “Degenerative adversarial neuroimage nets: Generating images that mimic disease progression”. MICCAI (2019), Springer, pp. 164–172.
– Zhang R., Ravì D., Yang G.-Z., Lo B. “A personalized air quality sensing system—a preliminary study on assessing the air quality of London underground stations”. IEEE BSN (2017), pp. 111–114.
– Ravì D., Wong C., Lo B., Yang G.-Z. “Deep learning for human activity recognition: A resource efficient implementation on low-power devices”. IEEE BSN (2016), pp. 71–76.
– Ravì D., Lo B., Yang G.-Z. “Real-Time Food Intake Classification and Energy Expenditure Estimation on a Mobile Device”. Body Sensor Network (2015).
– Battiato S., Farinella G.M., Guarnera M., Ravì D., Tomaselli V. “Instant scene recognition on mobile platform”. ECCV (2012), Springer, pp. 655–658.
– Battiato S., Farinella G.M., Puglisi G., Ravì D.. “Content-aware image resizing with seam selection based on gradient vector flow”. IEEE ICIP (2012), pp. 2117–2120.
– Battiato S., Farinella G.M., Guarnera M., Messina G., Ravì D.. “Red-eyes removal through cluster based Linear Discriminant Analysis”. IEEE ICIP (2010), pp. 2185–2188.
– Battiato S., Farinella G.M., Guarnera M., Messina G., Ravì D.. “Boosting gray codes for red eyes removal”. ICPR (2010), pp. 4214–4217.
– Battiato S., Farinella G.M., Gallo G., Ravì D.. “Spatial hierarchy of textons distributions for scene classification”. Intl. Conf. on Multimedia Modeling (2009), pp. 333–343.
– Battiato S., Farinella G.M., Gallo G., Ravì D.. “Scene categorization using bag of textons on spatial hierarchy”. IEEE ICIP (2008), pp. 2536–2539.
– Messina G., Ravì D., Guarnera M., Farinella G.M. “Method and apparatus for filtering red and/or golden eye artifacts”. U.S. Patent 8,498,496. July 2013.
White Paper
– Perez J.A., Deligianni F., Ravì D., Yang G.-Z., et al. “Artificial intelligence and robotics”. (2018).