Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Path: Self-supervision for Classification of Pathology Images with
  Limited Annotations   While high-resolution pathology images lend themselves well to `data hungry'
deep learning algorithms, obtaining exhaustive annotations on these images is a
major challenge. In this paper, we propose a self-supervised CNN approach to
leverage unlabeled data for learning generalizable and domain invariant
representations in pathology images. The proposed approach, which we term as
Self-Path, is a multi-task learning approach where the main task is tissue
classification and pretext tasks are a variety of self-supervised tasks with
labels inherent to the input data. We introduce novel domain specific
self-supervision tasks that leverage contextual, multi-resolution and semantic
features in pathology images for semi-supervised learning and domain
adaptation. We investigate the effectiveness of Self-Path on 3 different
pathology datasets. Our results show that Self-Path with the domain-specific
pretext tasks achieves state-of-the-art performance for semi-supervised
learning when small amounts of labeled data are available. Further, we show
that Self-Path improves domain adaptation for classification of histology image
patches when there is no labeled data available for the target domain. This
approach can potentially be employed for other applications in computational
pathology, where annotation budget is often limited or large amount of
unlabeled image data is available.

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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga