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MF-Hovernet: An Extension of Hovernet for Colon Nuclei Identification and Counting (CoNiC) Challenge

arXiv 2203.02161 ↗First posted 4 Mar 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Nuclei Identification and Counting is the most important morphological feature of cancers, especially in the colon. Many deep learning-based methods have been proposed to deal with this problem. In this work, we construct an extension of Hovernet for nuclei identification and counting to address the problem named MF-Hovernet. Our proposed model is the combination of multiple filer block to Hovernet architecture. The current result shows the efficiency of multiple filter block to improve the performance of the original Hovernet model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 4 Mar 2022.

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References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  3. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
  4. 2018Panoptic Segmentationcited by 802
  5. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
  6. 2019Dice Loss for Data-imbalanced NLP Taskscited by 182
  7. 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
  8. 2021CoNIC: Colon Nuclei Identification and Counting Challenge 2022cited by 45

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