DCAN: Deep contour‐aware networks for object instance segmentation from histology images

By Hao Chen, Xiaojuan Qi, Lequan Yu, Qi-Yan Dou, Jing Qin, P. Heng. Medical Image Anal., 2017

492 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/929c9a90e2ceac3f6684f2b29e6579c40b4ce3c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gary G. Yen
2. Zhang Yi
3. Daniel Cohen-Or
4. Simon Graham
5. Usha R. Acharya
6. Filippo Molinari
7. Kristen M. Meiburger
8. Massimo Salvi

In Computer Vision and Pattern Recognition

1. Daniel Cohen-Or
2. Dani Lischinski
3. Hui Huang
4. Luc Van Gool
5. Gary G. Yen
6. Ming-Hsuan Yang
7. Di Lin
8. Yu Qiao

Citations in the corpus

• Methods for Segmentation and Classification of Digital Microscopy Tissue Images (2018, methodology): “…Ghaznavi et al., 2013; Xie et al., 2015; Xu et al., 2015; Manivannan et al., 2016; Peikari and Martel, 2016; Sirinukunwattana et al., 2016; Wang et al., 2016; Xing and Yang, 2016; Al-Milaji et al., 2017; Chen et al., 2017; Zheng et al., 2017; Graham and Rajpoot, 2018; Senaras and Gurcan, 2018).”
• Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation (2020, background): “In addition, the size of available biomedical image dataset [14], [15] is relatively smaller than those used for generic image recognition, thus training a large network is prone to be overfitting.”
• The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “Chen et al. [188] proposed a novel deep contour-aware network to cope with the issue of merged nuclei.”
• Multi-scale Context Intertwining for Semantic Segmentation (2018, background): “, autonomous driving [4], medical analysis [5], and computational photography [6], can benefit from accurate semantic segmentation that provides detailed information about the content of an image.”
• Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images (2018, methodology): “The CNN is a supervised deep learning method that has been successfully applied in a large number of image analysis problems (Cireşan et al., 2013; Huang et al., 2016; Xie et al., 2015a, 2015b; Wang et al., 2016; Sirinukunwattana et al., 2016; Bayramoglu and Heikkila, 2016; Su et al., 2015; Hou et a”
• Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, background): “In addition, Chen et al. (2017b) summarized recent works on the use of hematoxylin and eosin (H&E) HP images for BrC prognosis.”