Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep
  Convolutional Neural Networks   We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus images, as well as giving the severity grades of DR. Moreover, since
retina lesions and DR severity appear with different scales in fundus images,
the integration of both local and global networks learn more complete and
specific features for DR analysis. (2) By introducing imbalanced weighting map,
more attentions will be given to lesion patches for DR grading, which
significantly improve the performance of the proposed algorithm. In this study,
we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
images from Kaggle competition dataset. Under the guidance of clinical
ophthalmologists, the experimental results show that our local lesion detection
net achieve comparable performance with trained human observers, and the
proposed imbalanced weighted scheme also be proved to significantly improve the
capability of our DCNN-based DR grading algorithm.

12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg