Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Contrastive Learning for Decentralized Unlabeled Medical
  Images   A label-efficient paradigm in computer vision is based on self-supervised
contrastive pre-training on unlabeled data followed by fine-tuning with a small
number of labels. Making practical use of a federated computing environment in
the clinical domain and learning on medical images poses specific challenges.
In this work, we propose FedMoCo, a robust federated contrastive learning (FCL)
framework, which makes efficient use of decentralized unlabeled medical data.
FedMoCo has two novel modules: metadata transfer, an inter-node statistical
data augmentation module, and self-adaptive aggregation, an aggregation module
based on representational similarity analysis. To the best of our knowledge,
this is the first FCL work on medical images. Our experiments show that FedMoCo
can consistently outperform FedAvg, a seminal federated learning framework, in
extracting meaningful representations for downstream tasks. We further show
that FedMoCo can substantially reduce the amount of labeled data required in a
downstream task, such as COVID-19 detection, to achieve a reasonable
performance.

31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org