Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BT-Unet: A self-supervised learning framework for biomedical image
  segmentation using Barlow Twins with U-Net models   Deep learning has brought the most profound contribution towards biomedical
image segmentation to automate the process of delineation in medical imaging.
To accomplish such task, the models are required to be trained using huge
amount of annotated or labelled data that highlights the region of interest
with a binary mask. However, efficient generation of the annotations for such
huge data requires expert biomedical analysts and extensive manual effort. It
is a tedious and expensive task, while also being vulnerable to human error. To
address this problem, a self-supervised learning framework, BT-Unet is proposed
that uses the Barlow Twins approach to pre-train the encoder of a U-Net model
via redundancy reduction in an unsupervised manner to learn data
representation. Later, complete network is fine-tuned to perform actual
segmentation. The BT-Unet framework can be trained with a limited number of
annotated samples while having high number of unannotated samples, which is
mostly the case in real-world problems. This framework is validated over
multiple U-Net models over diverse datasets by generating scenarios of a
limited number of labelled samples using standard evaluation metrics. With
exhaustive experiment trials, it is observed that the BT-Unet framework
enhances the performance of the U-Net models with significant margin under such
circumstances.

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The categories of this paper are: bayes-an
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
17
The categories of this paper are: q-fin