Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization

Table 6 Top ten most cited references

Rank	Title	Journal	First author	Year	Citations
1	Deep residual learning for image recognition	2016 IEEE Conference on Computer Vision and Pattern Recognition	Kaiming He	2016	149
2	U-Net: Convolutional networks for biomedical image segmentation	Medical Image Computing and Computer	Olaf Ronneberger	2015	124
3	Global cancer statistics	CA: A Cancer Journal for Clinicians	Ahmedin Jemal DVM	2011	117
4	Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study	PLoS Medicine	Jakob Nikolas Kather	2019	87
5	Very deep convolutional networks for large-scale image recognition	Computer Science	Karen Simonyan	2015	87
6	WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs saliency maps from physicians	Computerized Medical Imaging and Graphics	Jorge Bernal	2015	83
7	ImageNet classification with deep convolutional neural networks	Research-Article	Alex Krizhevsky	2017	81
8	Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer	Brief Communication	Jakob Nikolas Kather	2019	79
9	Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy	Gastroenterology	Gregor Urban	2018	75
10	Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries	CA: A Cancer Journal for Clinicians	Hyuna Sung	2021	72