Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UNetFormer: A Unified Vision Transformer Model and Pre-Training
  Framework for 3D Medical Image Segmentation   Vision Transformers (ViT)s have recently become popular due to their
outstanding modeling capabilities, in particular for capturing long-range
information, and scalability to dataset and model sizes which has led to
state-of-the-art performance in various computer vision and medical image
analysis tasks. In this work, we introduce a unified framework consisting of
two architectures, dubbed UNetFormer, with a 3D Swin Transformer-based encoder
and Convolutional Neural Network (CNN) and transformer-based decoders. In the
proposed model, the encoder is linked to the decoder via skip connections at
five different resolutions with deep supervision. The design of proposed
architecture allows for meeting a wide range of trade-off requirements between
accuracy and computational cost. In addition, we present a methodology for
self-supervised pre-training of the encoder backbone via learning to predict
randomly masked volumetric tokens using contextual information of visible
tokens. We pre-train our framework on a cohort of $5050$ CT images, gathered
from publicly available CT datasets, and present a systematic investigation of
various components such as masking ratio and patch size that affect the
representation learning capability and performance of downstream tasks. We
validate the effectiveness of our pre-training approach by fine-tuning and
testing our model on liver and liver tumor segmentation task using the Medical
Segmentation Decathlon (MSD) dataset and achieve state-of-the-art performance
in terms of various segmentation metrics. To demonstrate its generalizability,
we train and test the model on BraTS 21 dataset for brain tumor segmentation
using MRI images and outperform other methods in terms of Dice score. Code:
https://github.com/Project-MONAI/research-contributions

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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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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: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph