Selected publications of Lucia Maddalena

(BibTeX file)

L. Maddalena and L. Antonelli (Eds), Algorithms for Biomedical Image Analysis and Processing, Reprint of the Special Issue, Algorithms, ISBN: 978-3-0365-9760-7, DOI: 10.3390/books978-3-0365-9761-4, 2024.

M. Giordano, E. Falbo, L. Maddalena, M. Piccirillo, I. Granata, Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience, Biomolecules 14(1), ISSN: 2218-273X, DOI: 10.3390/biom14010018, 2024. Data available here.

L. Antonelli, L. Maddalena, Special Issue on "Algorithms for Biomedical Image Analysis and Processing", Algorithms 16(12), Editorial, DOI: 10.3390/a16120544, 2023.

L. Antonelli, F. Polverino, A. Albu, A. Hada, I.A. Asteriti, F. Degrassi, G. Guarguaglini, L. Maddalena, M.R. Guarracino, ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells, Scientific Data 10(677), DOI: 10.1038/s41597-023-02540-1, 2023. The ALFI dataset is available here.

- I. Granata, M. Giordano, L. Maddalena, M. Manzo, M.R. Guarracino, Network-Based Computational Modeling to Unravel Gene Essentiality, in R.P. Mondaini (Ed), Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics. BIOMAT 2022. Springer, Cham, DOI: 10.1007/978-3-031-33050-6_3, 2023.

- Khachatryan, L., Xiang, Y., Ivanov, A. et al., Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge, Scientific Reports 13, 6303 DOI: 10.1038/s41598-023-33050-0, 2023.

- M. Manzo, M. Giordano, L. Maddalena, M.R. Guarracino, I. Granata, Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis, In Dzemyda, G., Bernataviciene, J., Kacprzyk, J. (eds), Data Science in Applications. Studies in Computational Intelligence, Vol. 1084, Springer, Cham., DOI: 10.1007/978-3-031-24453-7_7, 2023. Related software and data available here.

- L. Maddalena, I. Granata, M. Giordano, M. Manzo, M.R. Guarracino, Integrating Different Data Modalities for the Classification of Alzheimer's Disease Stages, SN Computer Science, vol. 4, no. 249, DOI: 10.1007/s42979-023-01688-2, 2023. Related software and data available here. Free view-only version available here.

- I. Manipur, M. Giordano, M. Piccirillo, S. Parashuraman and L. Maddalena, Community Detection in Protein-Protein Interaction Networks and Applications, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 1, DOI: 10.1109/TCBB.2021.3138142, 2023. See also the Supplemental Material.

- L. Maddalena, M. Giordano, M. Manzo, M.R. Guarracino, Whole-Graph Embedding and Adversarial Attacks for Life Sciences, in R.P. Mondaini (Ed), Trends in Biomathematics: Stability and Oscillations in Environmental, Social, and Biological Models: Selected Works from the BIOMAT Consortium Lectures, Rio de Janeiro, Brazil, 2021, Springer International Publishing, DOI: 10.1007/978-3-031-12515-7_1, 1--21, 2022.

- I. Granata, I. Manipur, M. Giordano, L. Maddalena, M.R. Guarracino, TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models, Scientific Data, DOI: 10.1038/s41597-022-01702-x, vol. 9, no. 607, 2022. See the TumorMet repository.

- M. Giordano, L. Maddalena, M. Manzo, M.R. Guarracino, Adversarial attacks on graph-level embedding methods: a case study, Ann Math Artif Intell, DOI: 10.1007/s10472-022-09811-4, 2022. Free view-only version available here.

- L. Maddalena, L. Antonelli, A. Albu, A. Hada, M.R. Guarracino, Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-free Microscopy Imaging, Algorithms, DOI: 10.3390/a15090313, vol. 15, no. 313, 2022.

- I. Manipur, M. Manzo, I. Granata, M. Giordano, L. Maddalena, and M.R. Guarracino, Netpro2vec: a Graph Embedding Framework for Biomedical Applications , in IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: 10.1109/TCBB.2021.3078089, vol.19, no. 2, 2022. Related software available here.

- L. Maddalena, I. Granata, M. Giordano, M. Manzo, M.R. Guarracino, and for the Alzheimer's Disease Neuroimaging Initiative, Classifying Alzheimer's Disease Using MRIs and Transcriptomic Data, Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING, ISBN 978-989-758-552-4, DOI: 10.5220/0010902900003123, pages 70-79, 2022.

- M. Manzo, M. Giordano, L. Maddalena, and M.R. Guarracino, Performance Evaluation of Adversarial Attacks on Whole-Graph Embedding Models, In: D.E. Simos, P.M. Pardalos, and I.S. Kotsireas (Eds), Learning and Intelligent Optimization. Springer International Publishing, ISBN: 978-3-030-92121-7, DOI: 10.1007/978-3-030-92121-7_19, 219-236, 2021.

- L. Maddalena, I. Manipur, M. Manzo, and M.R. Guarracino, On Whole-Graph Embedding Techniques, In: Mondaini R.P. (Ed) Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells. BIOMAT 2020. Springer, Cham, DOI: 10.1007/978-3-030-73241-7_8, 115-131, 2021.

- L. Maddalena, I. Granata, I. Manipur, M. Manzo, and M.R. Guarracino, A Framework Based on Metabolic Networks and Biomedical Images Data to Discriminate Glioma Grades, in X. Ye et al. (Eds.), Biomedical Engineering Systems and Technologies. BIOSTEC 2020. CCIS 1400, Springer, ISBN: 978-3-030-72379-8, DOI: 10.1007/978-3-030-72379-8_9, 165--189, 2021.

- I. Manipur, I. Granata, L. Maddalena, and M.R. Guarracino, Network Distances for Weighted Digraphs, in Y. Kochetov et al. (Eds.), Mathematical Optimization Theory and Operations Research, CCIS 1275, Springer Nature Switzerland, ISBN: 978-3-030-58657-7, DOI: 10.1007/978-3-030-58657-7_31, 389-408, 2020.

- I. Manipur, I. Granata, L. Maddalena, M.R. Guarracino, Clustering analysis of tumor metabolic networks, BMC Bioinformatics, ISSN: 1471-2105, DOI: 10.1186/s12859-020-03564-9, Vol.21, n. 349, 2020.

- I. Granata, M.R. Guarracino, L. Maddalena, I. Manipur, and P.M. Pardalos, On Network Similarities and Their Applications, in R.P. Mondaini (Ed.), Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment: Selected Works from the BIOMAT Consortium Lectures, Szeged, Hungary, 2019, Springer International Publishing, ISBN: 978-3-030-46306-9, DOI: 10.1007/978-3-030-46306-9_3, 23-41, 2020.

- L. Maddalena, I. Granata, I. Manipur, M. Manzo, and M.R. Guarracino, Glioma Grade Classification via Omics Imaging, in F. Soares, A. Fred, and H. Gamboa (Eds.), Proceed. 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC2020), Volume 2: BIOIMAGING, ISBN: 978-989-758-398-8, 82-92, 2020 (Best Paper Award).

- I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos, Model simplification for supervised classification of metabolic networks, Annals of Mathematics and Artificial Intelligence, Springer, DOI: 10.1007/s10472-019-09640-y, Vol. 88, 91-104, 2020.

- L. Antonelli, M. R. Guarracino, L. Maddalena, and M. Sangiovanni, Integrating imaging and omics data: A review, Biomedical Signal Processing and Control, DOI: 10.1016/j.bspc.2019.04.032, Vol. 52, 264-280, 2019.

- L. Maddalena and A. Petrosino, Self-Organizing Background Subtraction Using Color and Depth Data, Multimedia Tools and Applications, Springer, DOI: 10.1007/s11042-018-6741-7, Vol. 78, No. 9, 11927--11948, 2019 (see the read-only pdf).

- M. R. Guarracino and L. Maddalena, SDI+: a Novel Algorithm for Segmenting Dermoscopic Images, IEEE Journal of Biomedical and Health Informatics, DOI: 10.1109/JBHI.2018.2808970, Vol. 23, No. 2, 481-488, 2019.

- I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos, Supervised Classification of Metabolic Networks, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, DOI: 10.1109/BIBM.2018.8621500, 2688-2693, 2018.

- L. Maddalena and A. Petrosino, Background Subtraction for Moving Object Detection in RGBD Data: A Survey, J. Imaging, DOI: 10.3390/jimaging4050071, Vol. 4, No. 5, 71, 2018.

- M. Camplani, L. Maddalena, G. Moyá Alcover, A. Petrosino, and L. Salgado, A Benchmarking Framework for Background Subtraction in RGBD Videos, in Battiato S., Farinella G., Leo M., Gallo G. (eds) New Trends in Image Analysis and Processing – ICIAP 2017. Lecture Notes in Computer Science, vol 10590, Springer, DOI: 10.1007/978-3-319-70742-6_21, 219-229, 2017 (see the read-only pdf).

- L. Maddalena and A. Petrosino, Exploiting Color and Depth for Background Subtraction, in Battiato S., Farinella G., Leo M., Gallo G. (eds) New Trends in Image Analysis and Processing – ICIAP 2017. Lecture Notes in Computer Science, vol 10590, Springer, DOI: 10.1007/978-3-319-70742-6_24, 254--265, 2017 (see the read-only pdf).

- P. M. Jodoin, L. Maddalena, A. Petrosino and Y. Wang, Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization, in IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2017.2728181, vol. 26, no. 11, 5244-5256, Nov. 2017.

- A. Petrosino, L. Maddalena, T. Bouwmans, Editorial–Scene background modeling and initialization, Pattern Recognition Letters, DOI: 10.1016/j.patrec.2017.05.032, Vol. 96, 1-2, 2017.

- T. Bouwmans, L. Maddalena, A. Petrosino, Scene background initialization: A taxonomy, Pattern Recognition Letters, DOI: 10.1016/j.patrec.2016.12.024, Vol. 96, 3-11, 2017.

- L. Maddalena, A. Petrosino, Extracting a Background Image by a Multi-modal Scene Background Model, 23rd International Conference on Pattern Recognition (ICPR), Cancun, DOI: 10.1109/ICPR.2016.7899623, 143-148, 2016.

- L. Maddalena, A. Petrosino, Towards Benchmarking Scene Background Initialization, in V. Murino et al. (eds), New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops, Lecture Notes in Computer Science, Vol. 9281, Springer International Publishing Switzerland, DOI: 10.1007/978-3-319-23222-5_57#, 469–476, 2015.

- L. Casalino, P. D’Ambra, M. R. Guarracino, A. Irpino, L. Maddalena, F. Maiorano, G. Minchiotti, E. J. Patriarca, Image Analysis and Classification for High-Throughput Screening of Embryonic Stem Cells, in V. Zazzu, M.B. Ferraro, and M.R. Guarracino (Eds.), Mathematical Models in Biology, Springer, 17-31, 2015.

- E. Di Nardo, L. Maddalena, A. Petrosino, Video-Based Access Control by Automatic License Plate Recognition, in S. Bassis, A. Esposito, and F.C. Morabito (Eds), Advances in Neural Networks: Computational and Theoretical Issues 37, Smart Innovation, Systems and Technologies, Springer International Publishing, DOI: 10.1007/978-3-319-18164-6_11, 103-117, 2015

- L. Maddalena, A. Petrosino, Towards Benchmarking Scene Background Initialization, arXiv:1506.04051, 2015.

- L. Maddalena, A. Petrosino, Background Model Initialization for Static Cameras, in T. Bouwmans, F. Porikli, B. Höferlin, and A. Vacavant (Eds), Background Modeling and Foreground Detection for Video Surveillance, DOI: 10.1201/b17223-5, 3-1–-3-16, Chapman and Hall/CRC 2014.

- L. Maddalena, A. Petrosino, The 3dSOBS+ algorithm for moving object detection, Computer Vision and Image Understanding, DOI: 10.1016/j.cviu.2013.11.006, vol. 122, 65–73, 125-134, 2014.

- L. Maddalena, A. Petrosino, F. Russo, People counting by learning their appearance in a multi-view camera environment, Pattern Recognition Letters, DOI: 10.1016/j.patrec.2013.10.006, vol. 36, 125-134, 2014.

- A. Ferone, L. Maddalena, Neural Background Subtraction for Pan-Tilt-Zoom Cameras, IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2013.2280121, vol. 44, no. 5, 571-579, 2014.

- A. Petrosino, L. Maddalena, P. Pala, et al. (Eds), New Trends in Image Analysis and Processing - ICIAP 2013 Workshops, Naples, Italy, September 2013, Proceedings, Series: Lecture Notes in Computer Science, vol. 8158, Springer, ISBN 978-3-642-41189-2, 2013.

- L. Maddalena, A. Petrosino, Stopped Object Detection by Learning Foreground Model in Videos, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2013.2242092, vol.24, no.5, 723-735, May 2013.

- L. Maddalena, A. Petrosino, The SOBS algorithm: What are the limits?, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), DOI: 10.1109/CVPRW.2012.6238922, 21-26, 16-21 June 2012.

- S.K. Pal, A. Petrosino, L. Maddalena (Eds), Handbook on Soft Computing for Video Surveillance, Chapman & Hall/CRC, ISBN: 9781439856840, 2012.

- L. Maddalena, A. Petrosino, Neural Networks in Video Surveillance: A Perspective View, in S.K. Pal, A. Petrosino, L. Maddalena (Eds), Handbook on Soft Computing for Video Surveillance, Chapman & Hall/CRC, ISBN: 9781439856840, 59-78, 2012.

- V. Cozza, M.R. Guarracino, L. Maddalena, A. Baroni, Dynamic Clustering Detection through Multi-valued Descriptors of Dermoscopic Images, Statistics in Medicine, John Wiley & Sons, Ltd., DOI: 10.1002/sim.4285, Vol. 30, Issue 20, 2536–2550, 2011. [Impact Factor: 2.328].

- G. Gemignani, L. Maddalena, A. Petrosino, Real-time Stopped Object Detection by Neural Dual Background Modeling, in Workshop Proceedings of Euro-Par 2010, Lecture Notes in Computer Science, Springer Berlin/Heidelberg, DOI: 10.1007/978-3-642-21878-1_44, Vol. 6586, 327-334, 2011.

- L. Maddalena, A. Petrosino, A Fuzzy Spatial Coherence-based Approach to Background/ Foreground Separation for Moving Object Detection, Neural Computing and Applications, Springer London, DOI: 10.1007/s00521-009-0285-8 (Published online on 23 June 2009), Vol. 19, 179–186, 2010 (see the read-only pdf). [Impact Factor: 0.627]

- L. Maddalena, A. Petrosino, G. Laccetti, A Fusion-based Approach to Digital Movie Restoration, Pattern Recognition, DOI: 10.1016/j.patcog.2008.10.026, Vol. 42, no. 7, 1485-1495, 2009. [Impact Factor: 3.279]

- L. Maddalena, A. Petrosino, A. Ferone, Object Motion Detection and Tracking by an Artificial Intelligence Approach, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 22, No. 5, World Scientific Publishing Company, Singapore, DOI: 10.1142/S0218001408006612, 915-928, 2008. [Impact Factor: 0.66]

- L. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2008.924285, Vol. 17, no. 7, 1168-1177, July 2008. [Impact Factor: 3.315]

- L. Maddalena, A. Petrosino, Restoration of Blue Scratches in Digital Image Sequences, Image and Vision Computing, Vol. 26, Elsevier, The Netherlands, 1314–1326, 2008. [Impact Factor: 1.496]

- L. Maddalena, A. Petrosino, Moving Object Detection for Real-Time Applications, in Proceedings of 14th International Conference on Image Analysis and Processing (ICIAP’07), IEEE Computer Society, Washington, DC, USA, ISBN 0-7695-2877-5, DOI: 10.1109/ICIAP.2007.89, 542-547, 2007.

- L. Maddalena, Efficient Methods for Scratch Removal in Image Sequences, in Proceedings of 11th International Conference on Image Analysis and Processing (ICIAP2001), IEEE Computer Society, ISBN 0-7695-1183-X, DOI: 10.1109/ICIAP.2001.957067, pp 547-552, 2001.

- D. di Serafino, L. Maddalena, P. Messina, A. Murli, Some Perspectives on High-Performance Mathematical Software, in R. De Leone, A. Murli, P.M. Pardalos, G. Toraldo (eds.), “High Performance Algorithms and Software in Nonlinear Optimization”, Kluwer Academic Publishers, ISBN 0-7923-5483-4, 1-23, 1998.

- D. di Serafino, L. Maddalena, A. Murli, PINEAPL: A European Project to Develop a Parallel Numerical Library for Industrial Applications, in C. Lengauer, M. Griebl, S. Gorlatch (eds.), “Euro-Par'97 Parallel Processing”, Lecture Notes in Computer Science, n. 1300, Springer, ISBN 3-540-63440-1, 1333-1339, 1997. [Impact Factor: 0.515]

- M. D'Apuzzo, L. Maddalena, Parallelization Strategies for the B-Spline Curve Interpolation Problem, in A. Le Méhauté, C. Rabut, L.L. Schumaker (eds.), “Curves and Surfaces with Applications in CAGD”, Vanderbilt University Press, ISBN 0-8265-1293-3, 1-8, 1997.

- M. D'Apuzzo, L. Maddalena, A Parallel Algorithm for Parametric Cubic B-Spline Curves Interpolation, in Neural, Parallel & Scientific Computing, Dynamic Publishers Inc, USA, ISSN 1061-5396, vol. 5, 201-220, 1997.

Last update: February 28, 2024