Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Advanced Deep Convolutional Neural Network Approaches for Digital
  Pathology Image Analysis: a comprehensive evaluation with different use cases   Deep Learning (DL) approaches have been providing state-of-the-art
performance in different modalities in the field of medical imagining including
Digital Pathology Image Analysis (DPIA). Out of many different DL approaches,
Deep Convolutional Neural Network (DCNN) technique provides superior
performance for classification, segmentation, and detection tasks. Most of the
task in DPIA problems are somehow possible to solve with classification,
segmentation, and detection approaches. In addition, sometimes pre and
post-processing methods are applied for solving some specific type of problems.
Recently, different DCNN models including Inception residual recurrent CNN
(IRRCNN), Densely Connected Recurrent Convolution Network (DCRCN), Recurrent
Residual U-Net (R2U-Net), and R2U-Net based regression model (UD-Net) have
proposed and provide state-of-the-art performance for different computer vision
and medical image analysis tasks. However, these advanced DCNN models have not
been explored for solving different problems related to DPIA. In this study, we
have applied these DCNN techniques for solving different DPIA problems and
evaluated on different publicly available benchmark datasets for seven
different tasks in digital pathology including lymphoma classification,
Invasive Ductal Carcinoma (IDC) detection, nuclei segmentation, epithelium
segmentation, tubule segmentation, lymphocyte detection, and mitosis detection.
The experimental results are evaluated with different performance metrics such
as sensitivity, specificity, accuracy, F1-score, Receiver Operating
Characteristics (ROC) curve, dice coefficient (DC), and Means Squired Errors
(MSE). The results demonstrate superior performance for classification,
segmentation, and detection tasks compared to existing machine learning and
DCNN based approaches.

8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg