Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable and synergistic deep learning for visual explanation and
  statistical estimations of segmentation of disease features from medical
  images   Deep learning (DL) models for disease classification or segmentation from
medical images are increasingly trained using transfer learning (TL) from
unrelated natural world images. However, shortcomings and utility of TL for
specialized tasks in the medical imaging domain remain unknown and are based on
assumptions that increasing training data will improve performance. We report
detailed comparisons, rigorous statistical analysis and comparisons of widely
used DL architecture for binary segmentation after TL with ImageNet
initialization (TII-models) with supervised learning with only medical
images(LMI-models) of macroscopic optical skin cancer, microscopic prostate
core biopsy and Computed Tomography (CT) DICOM images. Through visual
inspection of TII and LMI model outputs and their Grad-CAM counterparts, our
results identify several counter intuitive scenarios where automated
segmentation of one tumor by both models or the use of individual segmentation
output masks in various combinations from individual models leads to 10%
increase in performance. We also report sophisticated ensemble DL strategies
for achieving clinical grade medical image segmentation and model explanations
under low data regimes. For example; estimating performance, explanations and
replicability of LMI and TII models described by us can be used for situations
in which sparsity promotes better learning. A free GitHub repository of TII and
LMI models, code and more than 10,000 medical images and their Grad-CAM output
from this study can be used as starting points for advanced computational
medicine and DL research for biomedical discovery and applications.

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The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex