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ORCIDDeCaF/FAIR@MICCAI 2022: Singapore
- Shadi Albarqouni
, Spyridon Bakas, Sophia Bano, M. Jorge Cardoso, Bishesh Khanal, Bennett A. Landman, Xiaoxiao Li, Chen Qin, Islem Rekik, Nicola Rieke, Holger Roth, Debdoot Sheet, Daguang Xu:
Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health - Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings. Lecture Notes in Computer Science 13573, Springer 2022, ISBN 978-3-031-18522-9
Distributed, Collaborative, and Federated Learning
- Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John A. Onofrey, Lawrence H. Staib, James S. Duncan:
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. 3-16 - Yousef Yeganeh, Azade Farshad, Johann Boschmann, Richard Gaus, Maximilian Frantzen, Nassir Navab:
FedAP: Adaptive Personalization in Federated Learning for Non-IID Data. 17-27 - Huiyu Li, Nicholas Ayache, Hervé Delingette:
Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes? 28-36 - Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis:
Can Collaborative Learning Be Private, Robust and Scalable? 37-46 - Holger R. Roth, Ali Hatamizadeh, Ziyue Xu, Can Zhao, Wenqi Li, Andriy Myronenko, Daguang Xu:
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation. 47-57 - Chen Shen, Pochuan Wang, Dong Yang, Daguang Xu, Masahiro Oda, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Chiou-Shann Fuh, Kensaku Mori, Weichung Wang, Holger R. Roth:
Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning. 58-67 - Matteo Pennisi, Federica Proietto Salanitri, Simone Palazzo, Carmelo Pino, Francesco Rundo, Daniela Giordano, Concetto Spampinato:
GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging. 68-78 - Onat Dalmaz, Usama Mirza, Gökberk Elmas, Muzaffer Özbey, Salman Ul Hassan Dar, Tolga Çukur:
A Specificity-Preserving Generative Model for Federated MRI Translation. 79-88 - Malte Tölle, Ullrich Köthe, Florian André, Benjamin Meder, Sandy Engelhardt:
Content-Aware Differential Privacy with Conditional Invertible Neural Networks. 89-99 - Garima Aggarwal, Chun-Yin Huang, Di Fan, Xiaoxiao Li, Zehua Wang:
DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain. 100-109 - Seyedeh Maryam Hosseini, Milad Sikaroudi, Morteza Babaie, Hamid R. Tizhoosh:
Cluster Based Secure Multi-party Computation in Federated Learning for Histopathology Images. 110-118 - Sourav Kumar, Anantharaman Lakshminarayanan, Ken Chang, Feri Guretno, Ivan Ho Mien, Jayashree Kalpathy-Cramer, Pavitra Krishnaswamy, Praveer Singh:
Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling. 119-129 - Klaus Kades, Jonas Scherer, Maximilian Zenk, Marius Kempf, Klaus H. Maier-Hein:
Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana. 130-140 - Dimitris Stripelis, Umang Gupta, Nikhil J. Dhinagar, Greg Ver Steeg, Paul M. Thompson, José Luis Ambite:
Towards Sparsified Federated Neuroimaging Models via Weight Pruning. 141-151
Affordable AI and Healthcare
- Kaveri A. Thakoor, Ari Carter, Ge Song, Adam Wax, Omar Moussa, Royce W. S. Chen, Christine Hendon, Paul Sajda:
Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection. 155-167 - Germain Morilhat, Naomi Kifle, Sandra FinesilverSmith, Bram Ruijsink, Vittoria Vergani, Habtamu Tegegne Desita, Zerubabel Tegegne Desita, Esther Puyol-Antón, Aaron Carass, Andrew P. King:
Deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions. 168-177 - Rakshith Sathish, Swanand R. Khare, Debdoot Sheet:
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks. 178-189 - Ekansh Chauhan, Swathi Guptha, Likith Reddy, Raju S. Bapi:
LRH-Net: A Multi-level Knowledge Distillation Approach for Low-Resource Heart Network. 190-201
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