Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Continual Active Learning for Efficient Adaptation of Machine Learning
  Models to Changing Image Acquisition   Imaging in clinical routine is subject to changing scanner protocols,
hardware, or policies in a typically heterogeneous set of acquisition hardware.
Accuracy and reliability of deep learning models suffer from those changes as
data and targets become inconsistent with their initial static training set.
Continual learning can adapt to a continuous data stream of a changing imaging
environment. Here, we propose a method for continual active learning on a data
stream of medical images. It recognizes shifts or additions of new imaging
sources - domains -, adapts training accordingly, and selects optimal examples
for labelling. Model training has to cope with a limited labelling budget,
resembling typical real world scenarios. We demonstrate our method on
T1-weighted magnetic resonance images from three different scanners with the
task of brain age estimation. Results demonstrate that the proposed method
outperforms naive active learning while requiring less manual labelling.

5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-ph
16
The categories of this paper are: stat