Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the repeatability of deep learning models with Monte Carlo
  dropout   The integration of artificial intelligence into clinical workflows requires
reliable and robust models. Repeatability is a key attribute of model
robustness. Repeatable models output predictions with low variation during
independent tests carried out under similar conditions. During model
development and evaluation, much attention is given to classification
performance while model repeatability is rarely assessed, leading to the
development of models that are unusable in clinical practice. In this work, we
evaluate the repeatability of four model types (binary classification,
multi-class classification, ordinal classification, and regression) on images
that were acquired from the same patient during the same visit. We study the
performance of binary, multi-class, ordinal, and regression models on four
medical image classification tasks from public and private datasets: knee
osteoarthritis, cervical cancer screening, breast density estimation, and
retinopathy of prematurity. Repeatability is measured and compared on ResNet
and DenseNet architectures. Moreover, we assess the impact of sampling Monte
Carlo dropout predictions at test time on classification performance and
repeatability. Leveraging Monte Carlo predictions significantly increased
repeatability for all tasks on the binary, multi-class, and ordinal models
leading to an average reduction of the 95\% limits of agreement by 16% points
and of the disagreement rate by 7% points. The classification accuracy improved
in most settings along with the repeatability. Our results suggest that beyond
about 20 Monte Carlo iterations, there is no further gain in repeatability. In
addition to the higher test-retest agreement, Monte Carlo predictions were
better calibrated which leads to output probabilities reflecting more
accurately the true likelihood of being correctly classified.

16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex