Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection

Kaushik Das; Qianni Zhang

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arXiv · 2010.04416

Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection

Kaushik Das·Qianni Zhang

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Abstract

Since the beginning of this decade, CNN has been a very successful tool in the field of Computer Vision tasks.The invention of CNN was inspired from neuroscience and it shares a lot of anatomical similarities with our visual system.Inspired by the anatomyof humanvisual system, wearguethat the existing U-Net architecture can be improvedin many ways. As human visual system uses attention mechanism, we have used attention concatenation in place of normalconcatenation.Although, CNN is purely feed-forward in nature but anatomical evidences show that our brain contains recurrent synapses and they often outnumber feed-forward and top-down connections. Thisfact inspiresus to userecurrent convolution connectionsin place of normalconvolution blocksin U-Net.Thispaper also addressesthe class imbalance issuein the field of medical image analysis. The paperresolvestheproblem of class imbalanceswith the help of state-of-the-art loss functions.Weargue thatourproposed architecturecan be trained end to end with a few training data and it outperforms the other variantsof U-Net.

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BibTeX RIS

Kaushik Das, Qianni Zhang. 2020-10-09. Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection. https://arxiv.org/abs/2010.04416

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