Patch Network for medical image Segmentation

Weihu Song, Heng Yu, Jianhua Wu
2/23/2023
cs.CV

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Abstract

Accurate and fast segmentation of medical images is clinically essential, yet current research methods include convolutional neural networks with fast inference speed but difficulty in learning image contextual features, and transformer with good performance but high hardware requirements. In this paper, we present a Patch Network (PNet) that incorporates the Swin Transformer notion into a convolutional neural network, allowing it to gather richer contextual information while achieving the balance of speed and accuracy. We test our PNet on Polyp(CVC-ClinicDB and ETIS- LaribPolypDB), Skin(ISIC-2018 Skin lesion segmentation challenge dataset) segmentation datasets. Our PNet achieves SOTA performance in both speed and accuracy.

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Cite this paper

@article{song2023patch,
  title  = {Patch Network for medical image Segmentation},
  author = {Weihu Song and Heng Yu and Jianhua Wu},
  year   = {2023},
  eprint = {2302.11802},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.11802v1}
}

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