ICAR-CNR INdAM Research Unit

The ICAR-CNR INdAM Research Unit was established in 2013 through a cooperation agreement between the Istituto Nazionale di Alta Matematica ''Francesco Severi'' (INdAM) and the Istituto di Calcolo e Reti ad Alte Prestazioni of the National Research Council of Italy (ICAR-CNR).

Research topics

- Analysis, design, and implementation of methods, algorithms, and prototype software in high-performance computing environments for multimedia applications, with particular attention to images and image sequences. Applications include the detection of moving objects in RGBD videos, the segmentation of melanomas in dermoscopic images, and the high-throughput screening of stem cells in microscope images.

- 3D reconstruction and analysis of intranuclear proteins in fluorescence microscopy image sequences.

- Development of GPU algorithms for lineage analysis of yeast cells in phase contrast microscopy images in order to reduce time-to-solution.

- Implementation of computationally efficient Blockchain-based applications in contexts not strictly related to crypto-currencies.

- Development of weighted and weightless neural models (including deep neural networks) for feature extraction and classification in image processing, robotics, and bioinformatics domains.

- Metabolic Network Analysis.

Participants

- INdAM/GNCS Section on Numerical Analysis

 Laura Antonelli, Senior Research Scientist

Annabella Astorino, Research Scientist

- INdAM/GNCS Section on Fundamentals of Computer Science and Information Systems

 Maurizio Giordano, Research Scientist

Ilaria Granata, Research Scientist

Francesco Gregoretti, Research Scientist

Mario Rosario Guarracino, Associate Professor

Lucia Maddalena, Senior Research Scientist (ICAR-CNR INdAM R.U. Director)

Diego Romano, Research Scientist

Giovanni Schmid, Research Scientist (till 2015)

Scientific products

Software and datasets available online, produced within the scientific activities of the ICAR-CNR INdAM Research Unit

- Software for moving object detection in image sequences

-- SOBS
-- SC-SOBS
-- 3dSOBS+
-- RGBD-SOBS

- Software for skin lesion segmentation in dermoscopic images

-- SDI+

- Dataset for moving object detection in RGBD image sequences

-- SBM-RGBD dataset

- Dataset for background model initialization from image sequences

-- SBMnet
-- SBI

- Dataset of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models

-- TumorMet [36]

- Dataset for cell segmentation, event detection, and tracking for label-free microscopy imaging

-- ALFI [42]

Scientific initiatives sponsored by INdAM/GNCS

- Organization of Schools, Workshops, and Seminars

 Minisymposium MS11: Mathematical methods and tools for imaging problems in real-life applications, SIMAI 2023, Matera (Italy), August 29, 2023

Workshop How can Scientific Computing help to study Life Sciences?, Napoli (Italy) and online, September 13, 2021 [24] [25] [26]

Workshop Bringing Maths to Life - BMTL 2015, Napoli (Italy), October 19-21, 2015

Scene Background Modeling and Initialization (SBMI2015), Workshop in conjunction with ICIAP 2015, Genova (Italy), September 8th, 2015 [1]

Workshop Bringing Maths to Life - BMTL 2014, Napoli (Italy), October 29th, 2014

17th International Conference on Image Analysis and Processing (ICIAP 2013), Naples, September 9-13, 2013

- Participation to Conferences, Schools, Workshops, and Seminars

 GNCS 2024 Conference, Rimini (Italy), February 14-16, 2024

Parallel and Distributed Computing for Life Sciences: Algorithms, Methodologies, and Tools (PDCLifeS), Workshop of EURO-PAR 2019, Göttingen, Germany, August 26-27, 2019

The 9th International Conference on Network Analysis, Moskow, Russia, May 18-19, 2019

SIAM Conference on Imaging Science, Bologna, Italy, June 5-8, 2018 [5]

Background learning for detection and tracking from RGBD videos (RGBD2017), Workshop in conjunction with ICIAP 2017, Catania (Italy), September 11th, 2017 [11] [6] [4] [3]

Argonne Training Program on Extreme-Scale Computing (ATPESC), Pheasant Run Resort, Illinois, August 3-15, 2014

Publications acknowledging INdAM/GNCS

[45] L. Maddalena, 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.

[44] 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.

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

[42] 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.

[41] 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.

[40] 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.

[39] 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.

[38] 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.

[37] 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.

[36] 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.

[35] 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.

[34] 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.

[33] 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.

[32] 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, doi: 10.1109/TCBB.2021.3138142.

[31] 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 - Volume 2: BIOIMAGING, ISBN 978-989-758-552-4, ISSN 2184-4305, DOI: 10.5220/0010902900003123, pages 70-79, 2022.

[30] I. Manipur, M. Manzo, I. Granata, M. Giordano, L. Maddalena, and M.R. Guarracino, Netpro2vec: a Graph Embedding Framework for Biomedical Applications, GNCS-INdAM Research Day on Computational Intelligence Methods for Digital Health, Vietri sul Mare (SA) and online, December 21, 2021.

[29] M. Giordano, L. Maddalena, M. Manzo, and M.R. Guarracino, Adversarial Attacks on Graph Embedding: Applications in Computational Biology and Bioinformatics, Bioinformatics and Computational Biology Conference (BBCC 2021), online, December 1-3, 2021.

[28] L. Antonelli, E. Francomano, and F. Gregoretti, A CUDA-based implementation of an improved SPH method on GPU, Applied Mathematics and Computation, Elsevier, vol. 409, art. no. 125482, DOI: 10.1016/j.amc.2020.125482, 2021.

[27] L. Maddalena, M. Giordano, M. Manzo, and M.R. Guarracino, Whole-Graph Embedding and Adversarial Attacks for Life Sciences, keynote presentation at the 21th International Symposium on Mathematical and Computational Biology (BIOMAT 2021), online, November 1-5, 2021.

[26] L. Antonelli, F. Gregoretti, and G. Oliva, Identification and analysis of the intranuclear protein pattern in fluorescence microscopy images, Tech. Rep. RT-ICAR-NA-2021-02, 2021.

[25] M. Manzo, M. Giordano, L. Maddalena, and M.R. Guarracino, Whole graph embedding: robustness and vulnerability, Tech. Rep. RT-ICAR-NA-2021-01, 2021.

[24] M. Giordano, I. Granata, M.R. Guarracino, L. Maddalena, and M. Manzo, Graph Embedding for Biological Networks, presentation at the Workshop How can Scientific Computing help to study Life Sciences?, Napoli (Italy) and online, September 13, 2021.

[23] M. Manzo, M. Giordano, L. Maddalena, and M.R. Guarracino, Performance evaluation of adversarial attacks on whole-graph embedding models, 5th Learning and Intelligent Optimization (LION) conference, online, June 20-25, 2021.

[22] 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.

[21] 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.

[20] 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.

[19] L. Antonelli, V. De Simone, and D. di Serafino, Spatially Adaptive Regularization in Image Segmentation, Algorithms, vol. 13, no. 9, DOI: 10.3390/a13090226, 2020.

[18] 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.

[17] 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.

[16] L. Maddalena, M. Gori, and S.K. Pal, Pattern recognition and beyond: Alfredo Petrosino's scientific results, Pattern Recognition Letters, DOI: 10.1016/j.patrec.2020.07.032, 2020.

[15] 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, 23-41, ISBN: 978-3-030-46306-9, DOI: 10.1007/978-3-030-46306-9_3, 2020.

[14] 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).

[13] 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 88, Springer, 91-104, 2020.

[12] L. Antonelli, M. R. Guarracino, L. Maddalena, and M. Sangiovanni, Integrating imaging and omics data: A review, Biomedical Signal Processing and Control 52, 264–280, 2019.

[11] L. Maddalena and A. Petrosino, Self-Organizing Background Subtraction Using Color and Depth Data, Multimedia Tools and Applications 78(9), 11927--11948, Springer, 2019.

[10] M. R. Guarracino and L. Maddalena, SDI+: a Novel Algorithm for Segmenting Dermoscopic Images, IEEE Journal of Biomedical and Health Informatics, 23(2), 481-488, 2019.

[9] 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, 2688-2693, 2018.

[8] I. Manipur, I. Granata, L. Maddalena, K.P. Tripathi, and M.R.Guarracino, Clustering Analysis of Tumor Metabolic Networks, Bioinformatics and Computational Biology Conference (BBCC2018), Naples, November 2018.

[7]L Antonelli, V De Simone, Comparison of minimization methods for nonsmooth image segmentation, Communications in Applied and Industrial Mathematics 9 (1), 68-86, 2018.

[6] L. Maddalena and A. Petrosino, Background Subtraction for Moving Object Detection in RGBD Data: A Survey, J. Imaging, 4(5), 71, 2018.

[5] L. Casalino, M. R. Guarracino, and L. Maddalena, Imaging for High-Throughput Screening of Pluripotent Stem Cells, SIAM Conference on Imaging Science - IS18, Bologna, June 2018.

[4] 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.

[3] 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.

[2] L. Antonelli, V. De Simone, and D. di Serafino, On the application of the spectral projected gradient method in image segmentation, Journal of Mathematical Imaging and Vision, vol. 54, pp. 106-116, DOI: 10.1007/s10851-015-0591-y, 2016.

[1] 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.

Last update: March 6, 2024