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Gianmarco Santini
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Books and Theses
- 2019
- [b1]Gianmarco Santini:
Deep learning for medical image analysis. University of Pisa, Italy, 2019
Conference and Workshop Papers
- 2022
- [c5]Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Mathilde Colombié, Pascal Jézéquel, Mario Campone, Mathieu Rubeaux, Nicolas Normand:
Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies. EMBC 2022: 4736-4739 - 2021
- [c4]Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Pascal Jézéquel, Mario Campone, Nicolas Normand, Mathieu Rubeaux:
Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images. Medical Imaging: Computer-Aided Diagnosis 2021 - [c3]Gianmarco Santini, Yvon Nzoughet Obame, Constance Fourcade, Noémie Moreau, Mathieu Rubeaux:
Automatic classification of benign and malignant kidney masses using radiomics. A retrospective study exploiting the KiTS19 dataset. Medical Imaging: Image Processing 2021 - 2020
- [c2]Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Mario Campone, Mathilde Colombié, Mathieu Rubeaux, Nicolas Normand:
Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer*. EMBC 2020: 1532-1535 - [c1]Constance Fourcade, Ludovic Ferrer, Gianmarco Santini, Noémie Moreau, Caroline Rousseau, Marie Lacombe, Camille Guillerminet, Mathilde Colombié, Mario Campone, Diana Mateus, Mathieu Rubeaux:
Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images*. EMBC 2020: 1536-1539
Informal and Other Publications
- 2021
- [i6]Constance Fourcade, Ludovic Ferrer, Noémie Moreau, Gianmarco Santini, Aishlinn Brennan, Caroline Rousseau, Marie Lacombe, Vincent Fleury, Mathilde Colombié, Pascal Jézéquel, Mario Campone, Mathieu Rubeaux, Diana Mateus:
Deformable Image Registration with Deep Network Priors: a Study on Longitudinal PET Images. CoRR abs/2111.11873 (2021) - 2019
- [i5]Gianmarco Santini, Noémie Moreau, Mathieu Rubeaux:
Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge. CoRR abs/1909.00735 (2019) - 2018
- [i4]Gianmarco Santini, Lorena M. Zumbo, Nicola Martini
, Gabriele Valvano
, Andrea Leo, Andrea Ripoli, Francesco Avogliero, Dante Chiappino, Daniele Della Latta:
Synthetic contrast enhancement in cardiac CT with Deep Learning. CoRR abs/1807.01779 (2018) - [i3]Gabriele Valvano
, Gianmarco Santini, Nicola Martini
, Andrea Ripoli, Chiara Iacconi, Dante Chiappino, Daniele Della Latta:
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging. CoRR abs/1809.03788 (2018) - [i2]Gabriele Valvano
, Nicola Martini
, Andrea Leo, Gianmarco Santini, Daniele Della Latta, Emiliano Ricciardi, Dante Chiappino:
Training of a Skull-Stripping Neural Network with efficient data augmentation. CoRR abs/1810.10853 (2018) - 2017
- [i1]Gianmarco Santini, Daniele Della Latta, Nicola Martini
, Giuseppe Valvano, A. Gori, Andrea Ripoli, C. L. Susini, Luigi Landini, Dante Chiappino:
An automatic deep learning approach for coronary artery calcium segmentation. CoRR abs/1710.03023 (2017)
Coauthor Index
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