Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Generalized Deep Learning Framework for Whole-Slide Image Segmentation
  and Analysis   Histopathology tissue analysis is considered the gold standard in cancer
diagnosis and prognosis. Given the large size of these images and the increase
in the number of potential cancer cases, an automated solution as an aid to
histopathologists is highly desirable. In the recent past, deep learning-based
techniques have provided state of the art results in a wide variety of image
analysis tasks, including analysis of digitized slides. However, the size of
images and variability in histopathology tasks makes it a challenge to develop
an integrated framework for histopathology image analysis. We propose a deep
learning-based framework for histopathology tissue analysis. We demonstrate the
generalizability of our framework, including training and inference, on several
open-source datasets, which include CAMELYON (breast cancer metastases),
DigestPath (colon cancer), and PAIP (liver cancer) datasets. We discuss
multiple types of uncertainties pertaining to data and model, namely aleatoric
and epistemic, respectively. Simultaneously, we demonstrate our model
generalization across different data distribution by evaluating some samples on
TCGA data. On CAMELYON16 test data (n=139) for the task of lesion detection,
the FROC score achieved was 0.86 and in the CAMELYON17 test-data (n=500) for
the task of pN-staging the Cohen's kappa score achieved was 0.9090 (third in
the open leaderboard). On DigestPath test data (n=212) for the task of tumor
segmentation, a Dice score of 0.782 was achieved (fourth in the challenge). On
PAIP test data (n=40) for the task of viable tumor segmentation, a Jaccard
Index of 0.75 (third in the challenge) was achieved, and for viable tumor
burden, a score of 0.633 was achieved (second in the challenge). Our entire
framework and related documentation are freely available at GitHub and PyPi.

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The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
2
The categories of this paper are: math
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
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The categories of this paper are: nlin
31
The categories of this paper are: ao-sci