347 citations · 358 across the 7 of their papers we have counts for
8 papers
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
Frederic Jonske, Moon Kim, Enrico Nasca +8
It is an open secret that ImageNet is treated as the panacea of pretraining. Particularly in medical machine learning, models not trained from scratch are often finetuned based on…
Open-Source Skull Reconstruction with MONAI
Jianning Li, André Ferreira, Behrus Puladi +7
We present a deep learning-based approach for skull reconstruction for MONAI, which has been pre-trained on the MUG500+ skull dataset. The implementation follows the MONAI contribu…
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
Meirui Jiang, Xiaoxiao Li, Xiaofei Zhang +2
How to tackle non-iid data is a crucial topic in federated learning. This challenging problem not only affects training process, but also harms performance of clients not participa…
Federated Learning from Small Datasets
Michael Kamp, Jonas Fischer, Jilles Vreeken
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherentl…
Novelty Detection in Sequential Data by Informed Clustering and Modeling
Linara Adilova, Siming Chen, Michael Kamp
Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can b…
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang +2
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improv…