M
Maria Evelina Fantacci
Researcher at University of Pisa
Publications - 187
Citations - 3191
Maria Evelina Fantacci is an academic researcher from University of Pisa. The author has contributed to research in topics: Detector & Photon counting. The author has an hindex of 22, co-authored 182 publications receiving 2645 citations. Previous affiliations of Maria Evelina Fantacci include Istituto Nazionale di Fisica Nucleare.
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Journal ArticleDOI
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.
Arnaud Arindra Adiyoso Setio,Alberto Traverso,Thomas de Bel,Moira S.N. Berens,Cas van den Bogaard,Piergiorgio Cerello,Hao Chen,Qi Dou,Maria Evelina Fantacci,Bram Geurts,Robbert van der Gugten,Pheng-Ann Heng,Bart Jansen,Michael M.J. de Kaste,Valentin Kotov,Jack Yu-Hung Lin,Jeroen Manders,Alexander Sóñora-Mengana,Juan C. García-Naranjo,Evgenia Papavasileiou,Mathias Prokop,M. Saletta,Cornelia M. Schaefer-Prokop,Ernst T. Scholten,Luuk Scholten,Miranda M. Snoeren,Ernesto Lopez Torres,Jef Vandemeulebroucke,Nicole Walasek,Guido Zuidhof,Bram van Ginneken,Colin Jacobs +31 more
TL;DR: The LUNA16 challenge is described, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set, and the results so far are presented.
Journal ArticleDOI
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study
Bram van Ginneken,Bram van Ginneken,Samuel G. Armato,Bartjan de Hoop,Saskia van Amelsvoort-van de Vorst,Thomas Duindam,Meindert Niemeijer,Keelin Murphy,Arnold M. R. Schilham,Alessandra Retico,Maria Evelina Fantacci,Maria Evelina Fantacci,Niccolò Camarlinghi,Niccolò Camarlinghi,Francesco Bagagli,Francesco Bagagli,Ilaria Gori,Takeshi Hara,Hiroshi Fujita,G. Gargano,Roberto Bellotti,Sabina Tangaro,Lourdes Bolanos,Francesco De Carlo,Piergiorgio Cerello,S.C. Cheran,Ernesto Lopez Torres,Mathias Prokop,Mathias Prokop +28 more
TL;DR: ANODE09 is introduced, a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms, and it is demonstrated that combining the output of algorithms leads to marked performance improvements.
Journal ArticleDOI
Mammogram segmentation by contour searching and massive lesion classification with neural network
Donato Cascio,Francesco Fauci,R. Magro,Giuseppe Raso,Roberto Bellotti,F. De Carlo,Sabina Tangaro,G. De Nunzio,Maurizio Quarta,G. Forni,A. Lauria,Maria Evelina Fantacci,Alessandra Retico,Giovanni Luca Masala,Pietro Oliva,S. Bagnasco,S.C. Cheran,E. L. Torres +17 more
TL;DR: An algorithm for detecting masses in mammographic images acquired in several hospitals belonging to the MAGIC-5 collaboration by means of a ROI Hunter algorithm, without loss of meaningful information is presented.
Journal ArticleDOI
A completely automated CAD system for mass detection in a large mammographic database.
Roberto Bellotti,F. De Carlo,Sabina Tangaro,G. Gargano,G. Maggipinto,Marcello Castellano,R. Massafra,Donato Cascio,Francesco Fauci,R. Magro,Giuseppe Raso,A. Lauria,G. Forni,S. Bagnasco,P. Cerello,E. Zanon,S. C. Cheran,E. López Torres,Ubaldo Bottigli,Giovanni Luca Masala,Piernicola Oliva,Alessandra Retico,Maria Evelina Fantacci,Rosella Cataldo,I. De Mitri,G. De Nunzio +25 more
TL;DR: A completely automated classification system for the detection of masses in digitized mammographic images using a neural network and receiver operating characteristic and free-response ROC analysis was evaluated.
Journal ArticleDOI
Large scale validation of the M5L lung CAD on heterogeneous CT datasets
E. López Torres,Elisa Fiorina,Francesco Pennazio,Cristiana Peroni,M. Saletta,Niccolò Camarlinghi,Maria Evelina Fantacci,Piergiorgio Cerello +7 more
TL;DR: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large scale screenings and clinical programs, and the development of a dedicated module for GGOs detection could further improve it.