Items where City Author is "Tarroni, Giacomo"
Article
van de Venter, R., Skelton, E. ORCID: 0000-0003-0132-7948, Matthew, J. (2023).
Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study.
Insights Imaging, 14(1),
article number 25.
doi: 10.1186/s13244-023-01372-2
Chen, C., Qin, C., Ouyang, C. (2022). Enhancing MR image segmentation with realistic adversarial data augmentation. Medical Image Analysis, 82, article number 102597. doi: 10.1016/j.media.2022.102597
Zimmerer, D., Full, P. M, Isensee, F. (2022). MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images. IEEE Transactions on Medical Imaging, 41(10), pp. 2728-2738. doi: 10.1109/tmi.2022.3170077
Naval Marimont, S. & Tarroni, G. (2021). Implicit Field Learning for Unsupervised Anomaly Detection in Medical Images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12902, pp. 189-198. doi: 10.1007/978-3-030-87196-3_18
Guizzo, E., Weyde, T. ORCID: 0000-0001-8028-9905 & Tarroni, G.
ORCID: 0000-0002-0341-6138 (2021).
Anti-transfer learning for task invariance in convolutional neural networks for speech processing.
Neural Networks, 142,
pp. 238-251.
doi: 10.1016/j.neunet.2021.05.012
Bai, W., Suzuki, H., Huang, J. (2020). A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine, 26(10), pp. 1654-1662. doi: 10.1038/s41591-020-1009-y
Biffi, C., Cerrolaza, J. J., Tarroni, G. ORCID: 0000-0002-0341-6138 (2020).
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models.
IEEE Transactions on Medical Imaging, 39(6),
pp. 2088-2099.
doi: 10.1109/tmi.2020.2964499
Chen, C., Qin, C., Qiu, H. (2020). Deep Learning for Cardiac Image Segmentation: A Review. Frontiers in Cardiovascular Medicine, 7, article number 25. doi: 10.3389/fcvm.2020.00025
Tarroni, G. ORCID: 0000-0002-0341-6138, Bai, W., Oktay, O. (2020).
Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank.
Scientific Reports, 10(1),
article number 2408.
doi: 10.1038/s41598-020-58212-2
Biffi, C., Cerrolaza, J. J., Tarroni, G. ORCID: 0000-0002-0341-6138 (2019).
3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders.
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019-A,
pp. 1643-1646.
doi: 10.1109/isbi.2019.8759328
Tarroni, G. ORCID: 0000-0002-0341-6138, Oktay, O., Bai, W. (2019).
Learning-based quality control for cardiac MR images.
IEEE Transactions on Medical Imaging, 38(5),
pp. 1127-1138.
doi: 10.1109/tmi.2018.2878509
Bai, W., Sinclair, M., Tarroni, G. ORCID: 0000-0002-0341-6138 (2018).
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.
Journal of Cardiovascular Magnetic Resonanc, 20(1),
article number 65.
doi: 10.1186/s12968-018-0471-x
Conference or Workshop Item
Naval Marimont, S. ORCID: 0000-0002-7075-5586, Siomos, V., Baugh, M. (2024).
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024.
MICCAI 2024 27th International Conference, 6-10 Oct 2024, Marrakesh, Morocco.
doi: 10.1007/978-3-031-72120-5_23
Baugh, M. ORCID: 0000-0001-6252-7658, Reynaud, H.
ORCID: 0000-0003-0261-2660, Marimont, S. N.
ORCID: 0000-0002-7075-5586 (2024).
Image-Conditioned Diffusion Models for Medical Anomaly Detection.
In:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging.
UNSURE 2024, 10 Oct 2024, Marrakesh, Morocco.
doi: 10.1007/978-3-031-73158-7_11
Siomos, V., Naval-Marimont, S., Passerat-Palmbach, J. (2024). ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification. Paper presented at the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 27-30 May 2024, Athens, Greece. doi: 10.1109/isbi56570.2024.10635565
Marimont, S. N., Siomos, V. & Tarroni, G. ORCID: 0000-0002-0341-6138 (2024).
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images.
In:
Deep Generative Models.
Third MICCAI Workshop, DGM4MICCAI 2023, 8-12 Oct 2023, Vancouver, Canada.
doi: 10.1007/978-3-031-53767-7_4
Guizzo, E., Weyde, T. ORCID: 0000-0001-8028-9905, Tarroni, G.
ORCID: 0000-0002-0341-6138 (2023).
Quaternion Anti-Transfer Learning for Speech Emotion Recognition.
In:
2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).
2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 22-25 Oct 2023, New Paltz, NY, USA.
doi: 10.1109/WASPAA58266.2023.10248082
Siomos, V., Tarroni, G. ORCID: 0000-0002-0341-6138 & Passerrat-Palmbach, J. (2023).
FeTS Challenge 2022 Task 1: Implementing FedMGDA + and a New Partitioning.
In:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries.
8th International Workshop, BrainLes 2022, 18 Sep 2022, Singapore.
doi: 10.1007/978-3-031-44153-0_15
Marimont, S. N. & Tarroni, G. ORCID: 0000-0002-0341-6138 (2022).
Implicit U-Net for Volumetric Medical Image Segmentation.
In:
Lecture Notes in Computer Science.
Medical Image Understanding and Analysis 26th Annual Conference, MIUA 2022, 27-29 Jul 2022, Cambridge, UK.
doi: 10.1007/978-3-031-12053-4_29
Marimont, S. N. & Tarroni, G. ORCID: 0000-0002-0341-6138 (2021).
Implicit field learning for unsupervised anomaly detection in medical images.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.
MICCAI 2021, 27 Sep-1 Oct 2021, Strasbourg, France.
doi: 10.1007/978-3-030-87196-3_18
Marimont, S. N. & Tarroni, G. ORCID: 0000-0002-0341-6138 (2021).
Anomaly detection through latent space restoration using vector-quantized variational autoencoders.
In:
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).
IEEE ISBI 2021, 13-16 Apr 2021, Nice, France.
doi: 10.1109/ISBI48211.2021.9433778
Wang, S., Tarroni, G. ORCID: 0000-0002-0341-6138, Qin, C. (2020).
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.
23rd International Conference on Medical Image Computing and Computer Assisted Intervention, 04 - 08 October 2020, Lima, Peru.
doi: 10.1007/978-3-030-59719-1_9
Chen, C., Qin, C., Qiu, H. (2020). Realistic Adversarial Data Augmentation for MR Image Segmentation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, 04 - 08 October 2020, Lima, Peru. doi: 10.1007/978-3-030-59710-8_65
Chen, C., Ouyang, C., Tarroni, G. ORCID: 0000-0002-0341-6138 (2020).
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation.
In: Pop, M., Sermesant, M., Camara, O. (Eds.),
Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019.
STACOM 2019, 13 Oct 2019, Shenzen, China.
doi: 10.1007/978-3-030-39074-7_22
Bai, W., Chen, C., Tarroni, G. ORCID: 0000-0002-0341-6138 (2019).
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction.
In: Shen, D., Liu, T., Peters, T. M. (Eds.),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Lecture Notes in Computer Science, vol 11765.
Tarroni, G. ORCID: 0000-0002-0341-6138, Oktay, O., Sinclair, M. (2018).
A Comprehensive Approach for Learning-Based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018.
doi: 10.1007/978-3-030-00928-1_31
Biffi, C., Oktay, O., Tarroni, G. ORCID: 0000-0002-0341-6138 (2018).
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018.
doi: 10.1007/978-3-030-00934-2_52
Bai, W., Suzuki, H., Qin, C. (2018). Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. doi: 10.1007/978-3-030-00937-3_67
Other
Rajchl, M., Lee, M., Schrans, F. (2020). Learning under Distributed Weak Supervision.
Working Paper
Chen, C., Qin, C., Qiu, H. (2019). Deep learning for cardiac image segmentation: A review. City, university of London.