Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Min-Max Similarity: A Contrastive Learning Based Semi-Supervised
  Learning Network for Surgical Tools Segmentation   Segmentation of images is a popular topic in medical AI. This is mainly due
to the difficulty to obtain a significant number of pixel-level annotated data
to train a neural network. To address this issue, we proposed a semi-supervised
segmentation network based on contrastive learning. In contrast to the previous
state-of-the-art, we introduce a contrastive learning form of dual-view
training by employing classifiers and projectors to build all-negative, and
positive and negative feature pairs respectively to formulate the learning
problem as solving min-max similarity problem. The all-negative pairs are used
to supervise the networks learning from different views and make sure to
capture general features, and the consistency of unlabeled predictions is
measured by pixel-wise contrastive loss between positive and negative pairs. To
quantitative and qualitative evaluate our proposed method, we test it on two
public endoscopy surgical tool segmentation datasets and one cochlear implant
surgery dataset which we manually annotate the cochlear implant in surgical
videos. The segmentation performance (dice coefficients) indicates that our
proposed method outperforms state-of-the-art semi-supervised and fully
supervised segmentation algorithms consistently. The code is publicly available
at: https://github.com/AngeLouCN/Min_Max_Similarity

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The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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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: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin