| Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates G Valvano, A Leo, SA Tsaftaris IEEE Transactions on Medical Imaging, 2020 | 75 | 2020 |
| Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging G Valvano, G Santini, N Martini, A Ripoli, C Iacconi, D Chiappino, ... Journal of Healthcare Engineering 2019, 2019 | 71 | 2019 |
| An automatic deep learning approach for coronary artery calcium segmentation G Santini, DD Latta, N Martini, G Valvano, A Gori, A Ripoli, CL Susini, ... European Medical and Biological Engineering Confernce, 374-377, 2017 | 39 | 2017 |
| Measuring the Biases and Effectiveness of Content-Style Disentanglement X Liu, S Thermos, G Valvano, A Chartsias, A O’Neil, SA Tsaftaris British Machine Vision Conference (BMVC), 2021 | 21* | 2021 |
| Synthetic contrast enhancement in cardiac CT with Deep Learning G Santini, LM Zumbo, N Martini, G Valvano, A Leo, A Ripoli, F Avogliero, ... arXiv preprint arXiv:1807.01779, 2018 | 19 | 2018 |
| Evaluation of a Deep Convolutional Neural Network method for the segmentation of breast microcalcifications in Mammography Imaging G Valvano, D Della Latta, N Martini, G Santini, A Gori, C Iacconi, A Ripoli, ... EMBEC & NBC 2017: Joint Conference of the European Medical and Biological …, 2018 | 17 | 2018 |
| Temporal Consistency Objectives Regularize the Learning of Disentangled Representations G Valvano, A Chartsias, A Leo, SA Tsaftaris Domain Adaptation and Representation Transfer and Medical Image Learning …, 2019 | 12 | 2019 |
| Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts G Valvano, A Leo, SA Tsaftaris Journal of Machine Learning for Biomedical Imaging, 2022 | 8 | 2022 |
| Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training G Valvano, A Leo, SA Tsaftaris Domain Adaptation and Representation Transfer, 2021 | 8 | 2021 |
| Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning G Valvano, A Leo, SA Tsaftaris Domain Adaptation and Representation Transfer, 2021 | 5 | 2021 |
| Synthetic contrast enhancement in cardiac CT with deep learning,(2018) 1–8 G Santini, LM Zumbo, N Martini, G Valvano, A Leo, A Ripoli, F Avogliero, ... | 5 | 2018 |
| Regularizing disentangled representations with anatomical temporal consistency G Valvano, A Leo, SA Tsaftaris Biomedical Image Synthesis and Simulation, 325-346, 2022 | 1 | 2022 |
| Robust reconstruction of cardiac T1 maps using RNNs N Martini, A Vatti, A Ripoli, S Salaris, G Santini, G Valvano, MF Santarelli, ... Medical Imaging with Deep Learning, 2019 | 1 | 2019 |
| Automatic AHA model segmentation of cardiac T1 maps with deep learning N Martini, D Della Latta, G Santini, G Valvano, A Barison, F Avogliero, ... Proc Intl Soc Mag Reson Med 26, 1047, 2018 | 1 | 2018 |
| Controllable Image Synthesis of Industrial Data using Stable Diffusion G Valvano, A Agostino, G De Magistris, A Graziano, G Veneri Winter Conference on Applications of Computer Vision (WACV) 2024, 2023 | | 2023 |
| Semi-supervised and weakly-supervised learning with spatio-temporal priors in medical image segmentation G Valvano IMT School for Advanced Studies Lucca, 2021 | | 2021 |
| Measuring the Biases and Effectiveness of Content-Style Disentanglement (Supplementary Material) X Liu, S Thermos, G Valvano, A Chartsias, A O’Neil, SA Tsaftaris | | 2021 |
| Sviluppo di un sistema di Deep Learning per segmentazione di immagini mammografiche G VALVANO University of Pisa, 2017 | | 2017 |
Sotirios TsaftarisChair in Machine Learning and Computer Vision, The University of EdinburghVerified email at ed.ac.uk
