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Transformation Invariant Cancerous Tissue Classification Using Spatially Transformed DenseNet

Image and Video Processing 2022-04-26 v1 Computer Vision and Pattern Recognition Machine Learning
Authors: Omar Mahdi , Ali Bou Nassif
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Abstract

In this work, we introduce a spatially transformed DenseNet architecture for transformation invariant classification of cancer tissue. Our architecture increases the accuracy of the base DenseNet architecture while adding the ability to operate in a transformation invariant way while simultaneously being simpler than other models that try to provide some form of invariance.

Keywords

cancer detection and segmentation breast cancer detection medical image segmentation

Cite

@article{arxiv.2204.11066,
  title  = {Transformation Invariant Cancerous Tissue Classification Using Spatially Transformed DenseNet},
  author = {Omar Mahdi and Ali Bou Nassif},
  journal= {arXiv preprint arXiv:2204.11066},
  year   = {2022}
}

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