Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalization of Deep Convolutional Neural Networks -- A Case-study on
  Open-source Chest Radiographs   Deep Convolutional Neural Networks (DCNNs) have attracted extensive attention
and been applied in many areas, including medical image analysis and clinical
diagnosis. One major challenge is to conceive a DCNN model with remarkable
performance on both internal and external data. We demonstrate that DCNNs may
not generalize to new data, but increasing the quality and heterogeneity of the
training data helps to improve the generalizibility factor. We use
InceptionResNetV2 and DenseNet121 architectures to predict the risk of 5 common
chest pathologies. The experiments were conducted on three publicly available
databases: CheXpert, ChestX-ray14, and MIMIC Chest Xray JPG. The results show
the internal performance of each of the 5 pathologies outperformed external
performance on both of the models. Moreover, our strategy of exposing the
models to a mix of different datasets during the training phase helps to
improve model performance on the external dataset.

12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat