Research progress on colorectal cancer identification based on convolutional neural network_Journal of Biomedical Engineering

Authors：

PAN Xingliang ^1,2 , TONG Ke ^1,2 , YAN Chengdong ^1,2 , LUO Jinlong ³ , YANG Hua ³ ,  DING Jurong ^1,2

1. The School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China;
2. The Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, P. R. China;
3. The Department of general surgery, Zigong fourth people’s hospital, Zigong, Sichuan 643000, P. R. China;

Corresponding author：

Keywords：

Convolutional neural network; Medical image; Colorectal cancer classification; Colorectal cancer segmentation; Optimization method

DOI：

10.7507/1001-5515.202310027

Video：

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Colorectal cancer (CRC) is a common malignant tumor that seriously threatens human health. CRC presents a formidable challenge in terms of accurate identification due to its indistinct boundaries. With the widespread adoption of convolutional neural networks (CNNs) in image processing, leveraging CNNs for automatic classification and segmentation holds immense potential for enhancing the efficiency of colorectal cancer recognition and reducing treatment costs. This paper explores the imperative necessity for applying CNNs in clinical diagnosis of CRC. It provides an elaborate overview on research advancements pertaining to CNNs and their improved models in CRC classification and segmentation. Furthermore, this work summarizes the ideas and common methods for optimizing network performance and discusses the challenges faced by CNNs as well as future development trends in their application towards CRC classification and segmentation, thereby promoting their utilization within clinical diagnosis.

Citation： PAN Xingliang, TONG Ke, YAN Chengdong, LUO Jinlong, YANG Hua, DING Jurong. Research progress on colorectal cancer identification based on convolutional neural network. Journal of Biomedical Engineering, 2024, 41(4): 854-860. doi: 10.7507/1001-5515.202310027 Copy

1.	Liang F, Wang S, Zhang K, et al. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World Journal of Gastrointestinal Oncology, 2022, 14(1): 124-152.
2.	周雄, 胡明, 李子帅, 等. 2020年全球及中国结直肠癌流行状况分析. 海军军医大学学报, 2022, 43(12): 1356-1364.
3.	Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249.
4.	Davri A, Birbas E, Kanavos T, et al. Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review. Diagnostics, 2022, 12(4): 837.
5.	石磊, 籍庆余, 陈清威, 等. 视觉Transformer在医学图像分析中的应用研究综述. 计算机工程与应用, 2023, 59(8): 41-55.
6.	Tsai M J, Tao Y H. Deep learning technology applied to medical image tissue classification. Diagnostics, 2022, 12(10): 2430.
7.	Kather J N, Krisam J, Charoentong P, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Medicine, 2019, 16(1): e1002730.
8.	Yamashita R, Long J, Longacre T, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. The Lancet Oncology, 2021, 22(1): 132-141.
9.	Cao R, Yang F, Ma S C, et al. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer. Theranostics, 2020, 10(24): 11080-11091.
10.	Tsai M J, Tao Y H. Deep learning techniques for the classification of colorectal cancer tissue. Electronics, 2021, 10(14): 1662.
11.	Qadir H A, Shin Y, Solhusvik J, et al. Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction. Medical Image Analysis, 2021, 68: 101897.
12.	Tang C P, Chang H Y, Wang W C, et al. A novel computer-aided detection/diagnosis system for detection and classification of polyps in colonoscopy. Diagnostics, 2023, 13(2): 170.
13.	Sarwinda D, Paradisa R H, Bustamam A, et al. Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Computer Science, 2021, 179: 423-431.
14.	Ellahyani A, Jaafari I E, Charfi S, et al. Fine-tuned deep neural networks for polyp detection in colonoscopy images. Personal and Ubiquitous Computing, 2023, 27(2): 235-247.
15.	Taş M, Yılmaz B. Super resolution convolutional neural network based pre-processing for automatic polyp detection in colonoscopy images. Computers & Electrical Engineering, 2021, 90: 106959.
16.	Nisha J S, Gopi V P, Palanisamy P. Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomedical Signal Processing and Control, 2022, 73: 103465.
17.	Carrinho P, Falcao G. Highly accurate and fast YOLOv4-based polyp detection. Expert Systems with Applications, 2023, 232: 120834.
18.	Chen H, Li C, Li X, et al. IL-MCAM: an interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach. Computers in Biology and Medicine, 2022, 143: 105265.
19.	Zhou P, Cao Y, Li M, et al. HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism. Scientific Reports, 2022, 12(1): 15103.
20.	Dabass M, Vashisth S, Vig R. A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Computers in Biology and Medicine, 2022, 147: 105680.
21.	Zeid M A E, El-Bahnasy K, Abo-Youssef S E. Multiclass colorectal cancer histology images classification using vision transformers//Proceedings of 2021 Tenth International Conference on Intelligent Computing and Information Systems, Cairo, Egypt: IEEE, 2021: 224-230.
22.	Ma Weijie, Zhu Ye, Zhang Ruimao, et al. Toward clinically assisted colorectal polyp recognition via structured cross-modal representation consistency//Proceedings of Medical Image Computing and Computer Assisted Intervention(MICCAI 2022), Singapore: Springer, 2022, 13433: 141-150.
23.	Chang X, Wang J, Zhang G, et al. Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural network. Cell Reports Medicine, 2023, 4(2): 100914.
24.	Wang K S, Yu G, Xu C, et al. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Medicine, 2021, 19(1): 76.
25.	Koziarski M. Two-stage resampling for convolutional neural network training in the imbalanced colorectal cancer image classification//Proceedings of 2021 International Joint Conference on Neural Networks, Shenzhen, China: IEEE, 2021: 1-8.
26.	Yao Y, Gou S, Tian R, et al. Automated classification and segmentation in colorectal images based on self-paced transfer network. BioMed Research International, 2021, 2021: 6683931.
27.	Ohata E F, Chagas J V S, Bezerra G M, et al. A novel transfer learning approach for the classification of histological images of colorectal cancer. The Journal of Supercomputing, 2021, 77(9): 9494-9519.
28.	Zhou C, Jin Y, Chen Y, et al. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Computerized Medical Imaging and Graphics, 2021, 88: 101861.
29.	Kumar A, Vishwakarma A, Bajaj V. CRCCN-Net: automated framework for classification of colorectal tissue using histopathological images. Biomedical Signal Processing and Control, 2023, 79(2): 104172.
30.	Chen B L, Wan J J, Chen T Y, et al. A self-attention based faster R-CNN for polyp detection from colonoscopy images. Biomedical Signal Processing and Control, 2021, 70: 103019.
31.	Ma Conghui, Jiang Huiqin, Ma Ling, et al. A real-time polyp detection framework for colonoscopy video//Proceedings of Pattern Recognition and Computer Vision: 5th Chinese Conference, Shenzhen, China: Springer, 2022: 267-278.
32.	Nogueira-Rodríguez A, Domínguez-Carbajales R, Campos-Tato F, et al. Real-time polyp detection model using convolutional neural networks. Neural Computing and Applications, 2022, 34(13): 10375-10396.
33.	Bian H, Jiang M, Qian J. The investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system. PLoS One, 2023, 18(7): e0288376.
34.	Xu Jianwei, Zhao Ran, Yu Yizhou, et al. Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit. Biomedical Signal Processing and Control, 2021, 66: 102503.
35.	Zhu H T, Zhang X Y, Shi Y J, et al. Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net. Journal of Applied Clinical Medical Physics, 2021, 22(9): 324-331.
36.	Bokhorst J M, Nagtegaal I D, Fraggetta F, et al. Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images. Scientific Reports, 2023, 13(1): 8398.
37.	Narasimha Raju A S, Jayavel K, Rajalakshmi T. Dexterous identification of carcinoma through ColoRectalCADx with dichotomous fusion CNN and UNet semantic segmentation. Computational Intelligence and Neuroscience, 2022, 2022: 4325412.
38.	Narasimha Raju A S, Jayavel K, Rajalakshmi T. ColoRectalCADx: expeditious recognition of colorectal cancer with integrated convolutional neural networks and visual explanations using mixed dataset evidence. Computational and Mathematical Methods in Medicine 2022, 2022: 8723957.
39.	Panic J, Defeudis A, Mazzetti S, et al. A convolutional neural network based system for colorectal cancer segmentation on MRI images. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 1675-1678.
40.	Zhang K, Fu J H, Hua L, et al. Multiple morphological constraints-based complex gland segmentation in colorectal cancer pathology image analysis. Complexity, 2020, 2020: 6180457.
41.	Hosseinzadeh Kassani S, Hosseinzadeh Kassani P, Wesolowski M J, et al. Deep transfer learning based model for colorectal cancer histopathology segmentation: a comparative study of deep pre-trained models. International Journal of Medical Informatics, 2022, 159: 104669.
42.	Shah N A, Gupta D, Lodaya R, et al. Colorectal cancer segmentation using atrous convolution and residual enhanced UNet//Proceedings of Computer Vision and Image Processing, Prayagraj, India: Springer, 2021, 1376: 451-462.
43.	贾立新, 胡奕标, 金燕, 等. 融合多种注意力机制的结直肠息肉分割神经网络. 计算机辅助设计与图形学学报, 2023, 35(3): 463-473.
44.	Huang Y J, Dou Q, Wang Z X, et al. 3D RoI-aware U-Net for accurate and efficient colorectal tumor segmentation. IEEE Transactions on Cybernetics, 2021, 51(11): 5397-5408.
45.	Zidan U, Gaber M M, Abdelsamea M M. SwinCup: cascaded swin transformer for histopathological structures segmentation in colorectal cancer. Expert Systems with Applications, 2023, 216: 119452.
46.	Wang P, Chung A C S. DoubleU-Net: colorectal cancer diagnosis and gland instance segmentation with text-guided feature control//Proceedings of European Conference on Computer Vision–ECCV 2020 Workshops, Glasgow, UK: Springer, 2020: 338-354.
47.	Zhang Y D, Liu H Y, Hu Q. TransFuse: fusing transformers and CNNs for medical image segmentation//Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Strasbourg, France: Springer, 2021: 14-24.
48.	Zhang H, Yang X, Li D, et al. Dual parallel net: a novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian mixture prior. Journal of Biomedical Informatics, 2023, 139: 104304.
49.	Akilandeswari A, Sungeetha D, Joseph C, et al. Automatic detection and segmentation of colorectal cancer with deep residual convolutional neural network. Evidence-Based Complementary and Alternative Medicine, 2022, 2022: 3415603.
50.	González-Bueno Puyal J, Brandao P, Ahmad O F, et al. Polyp detection on video colonoscopy using a hybrid 2D/3D CNN. Medical Image Analysis, 2022, 82: 102625.
51.	Zheng S, Lin X, Zhang W, et al. MDCC-Net: multiscale double-channel convolution U-Net framework for colorectal tumor segmentation. Computers in Biology and Medicine, 2021, 130: 104183.
52.	Li D G, Chu X, Cui Y, et al. Improved U-Net based on contour prediction for efficient segmentation of rectal cancer. Computer Methods and Programs in Biomedicine, 2022, 213: 106493.
53.	Yue G, Zhuo G, Yan W, et al. Boundary uncertainty aware network for automated polyp segmentation. Neural Networks, 2024, 170: 390-404.
54.	Yeung M, Sala E, Schönlieb C B, et al. Focus U-Net: a novel dual attention-gated CNN for polyp segmentation during colonoscopy. Computers in Biology and Medicine, 2021, 137: 104815.
55.	Lu Jiaqi, Liu Ruiqing, Zhang Yuejuan, et al. Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence. Intelligent Medicine, 2022, 2(2): 82-87.
56.	Kusters K C, Scheeve T, Dehghani N, et al. Colorectal polyp classification using confidence-calibrated convolutional neural networks//Proceedings of Medical Imaging 2022: Computer-Aided Diagnosis, SPIE, 2022, 12033: 442-454.
57.	Paladini E, Vantaggiato E, Bougourzi F, et al. Two ensemble-CNN approaches for colorectal cancer tissue type classification. Journal of Imaging, 2021, 7(3): 51.

1. Liang F, Wang S, Zhang K, et al. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World Journal of Gastrointestinal Oncology, 2022, 14(1): 124-152.
2. 周雄, 胡明, 李子帅, 等. 2020年全球及中国结直肠癌流行状况分析. 海军军医大学学报, 2022, 43(12): 1356-1364.
3. Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249.
4. Davri A, Birbas E, Kanavos T, et al. Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review. Diagnostics, 2022, 12(4): 837.
5. 石磊, 籍庆余, 陈清威, 等. 视觉Transformer在医学图像分析中的应用研究综述. 计算机工程与应用, 2023, 59(8): 41-55.
6. Tsai M J, Tao Y H. Deep learning technology applied to medical image tissue classification. Diagnostics, 2022, 12(10): 2430.
7. Kather J N, Krisam J, Charoentong P, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Medicine, 2019, 16(1): e1002730.
8. Yamashita R, Long J, Longacre T, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. The Lancet Oncology, 2021, 22(1): 132-141.
9. Cao R, Yang F, Ma S C, et al. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer. Theranostics, 2020, 10(24): 11080-11091.
10. Tsai M J, Tao Y H. Deep learning techniques for the classification of colorectal cancer tissue. Electronics, 2021, 10(14): 1662.
11. Qadir H A, Shin Y, Solhusvik J, et al. Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction. Medical Image Analysis, 2021, 68: 101897.
12. Tang C P, Chang H Y, Wang W C, et al. A novel computer-aided detection/diagnosis system for detection and classification of polyps in colonoscopy. Diagnostics, 2023, 13(2): 170.
13. Sarwinda D, Paradisa R H, Bustamam A, et al. Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Computer Science, 2021, 179: 423-431.
14. Ellahyani A, Jaafari I E, Charfi S, et al. Fine-tuned deep neural networks for polyp detection in colonoscopy images. Personal and Ubiquitous Computing, 2023, 27(2): 235-247.
15. Taş M, Yılmaz B. Super resolution convolutional neural network based pre-processing for automatic polyp detection in colonoscopy images. Computers & Electrical Engineering, 2021, 90: 106959.
16. Nisha J S, Gopi V P, Palanisamy P. Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomedical Signal Processing and Control, 2022, 73: 103465.
17. Carrinho P, Falcao G. Highly accurate and fast YOLOv4-based polyp detection. Expert Systems with Applications, 2023, 232: 120834.
18. Chen H, Li C, Li X, et al. IL-MCAM: an interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach. Computers in Biology and Medicine, 2022, 143: 105265.
19. Zhou P, Cao Y, Li M, et al. HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism. Scientific Reports, 2022, 12(1): 15103.
20. Dabass M, Vashisth S, Vig R. A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Computers in Biology and Medicine, 2022, 147: 105680.
21. Zeid M A E, El-Bahnasy K, Abo-Youssef S E. Multiclass colorectal cancer histology images classification using vision transformers//Proceedings of 2021 Tenth International Conference on Intelligent Computing and Information Systems, Cairo, Egypt: IEEE, 2021: 224-230.
22. Ma Weijie, Zhu Ye, Zhang Ruimao, et al. Toward clinically assisted colorectal polyp recognition via structured cross-modal representation consistency//Proceedings of Medical Image Computing and Computer Assisted Intervention(MICCAI 2022), Singapore: Springer, 2022, 13433: 141-150.
23. Chang X, Wang J, Zhang G, et al. Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural network. Cell Reports Medicine, 2023, 4(2): 100914.
24. Wang K S, Yu G, Xu C, et al. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Medicine, 2021, 19(1): 76.
25. Koziarski M. Two-stage resampling for convolutional neural network training in the imbalanced colorectal cancer image classification//Proceedings of 2021 International Joint Conference on Neural Networks, Shenzhen, China: IEEE, 2021: 1-8.
26. Yao Y, Gou S, Tian R, et al. Automated classification and segmentation in colorectal images based on self-paced transfer network. BioMed Research International, 2021, 2021: 6683931.
27. Ohata E F, Chagas J V S, Bezerra G M, et al. A novel transfer learning approach for the classification of histological images of colorectal cancer. The Journal of Supercomputing, 2021, 77(9): 9494-9519.
28. Zhou C, Jin Y, Chen Y, et al. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Computerized Medical Imaging and Graphics, 2021, 88: 101861.
29. Kumar A, Vishwakarma A, Bajaj V. CRCCN-Net: automated framework for classification of colorectal tissue using histopathological images. Biomedical Signal Processing and Control, 2023, 79(2): 104172.
30. Chen B L, Wan J J, Chen T Y, et al. A self-attention based faster R-CNN for polyp detection from colonoscopy images. Biomedical Signal Processing and Control, 2021, 70: 103019.
31. Ma Conghui, Jiang Huiqin, Ma Ling, et al. A real-time polyp detection framework for colonoscopy video//Proceedings of Pattern Recognition and Computer Vision: 5th Chinese Conference, Shenzhen, China: Springer, 2022: 267-278.
32. Nogueira-Rodríguez A, Domínguez-Carbajales R, Campos-Tato F, et al. Real-time polyp detection model using convolutional neural networks. Neural Computing and Applications, 2022, 34(13): 10375-10396.
33. Bian H, Jiang M, Qian J. The investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system. PLoS One, 2023, 18(7): e0288376.
34. Xu Jianwei, Zhao Ran, Yu Yizhou, et al. Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit. Biomedical Signal Processing and Control, 2021, 66: 102503.
35. Zhu H T, Zhang X Y, Shi Y J, et al. Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net. Journal of Applied Clinical Medical Physics, 2021, 22(9): 324-331.
36. Bokhorst J M, Nagtegaal I D, Fraggetta F, et al. Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images. Scientific Reports, 2023, 13(1): 8398.
37. Narasimha Raju A S, Jayavel K, Rajalakshmi T. Dexterous identification of carcinoma through ColoRectalCADx with dichotomous fusion CNN and UNet semantic segmentation. Computational Intelligence and Neuroscience, 2022, 2022: 4325412.
38. Narasimha Raju A S, Jayavel K, Rajalakshmi T. ColoRectalCADx: expeditious recognition of colorectal cancer with integrated convolutional neural networks and visual explanations using mixed dataset evidence. Computational and Mathematical Methods in Medicine 2022, 2022: 8723957.
39. Panic J, Defeudis A, Mazzetti S, et al. A convolutional neural network based system for colorectal cancer segmentation on MRI images. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 1675-1678.
40. Zhang K, Fu J H, Hua L, et al. Multiple morphological constraints-based complex gland segmentation in colorectal cancer pathology image analysis. Complexity, 2020, 2020: 6180457.
41. Hosseinzadeh Kassani S, Hosseinzadeh Kassani P, Wesolowski M J, et al. Deep transfer learning based model for colorectal cancer histopathology segmentation: a comparative study of deep pre-trained models. International Journal of Medical Informatics, 2022, 159: 104669.
42. Shah N A, Gupta D, Lodaya R, et al. Colorectal cancer segmentation using atrous convolution and residual enhanced UNet//Proceedings of Computer Vision and Image Processing, Prayagraj, India: Springer, 2021, 1376: 451-462.
43. 贾立新, 胡奕标, 金燕, 等. 融合多种注意力机制的结直肠息肉分割神经网络. 计算机辅助设计与图形学学报, 2023, 35(3): 463-473.
44. Huang Y J, Dou Q, Wang Z X, et al. 3D RoI-aware U-Net for accurate and efficient colorectal tumor segmentation. IEEE Transactions on Cybernetics, 2021, 51(11): 5397-5408.
45. Zidan U, Gaber M M, Abdelsamea M M. SwinCup: cascaded swin transformer for histopathological structures segmentation in colorectal cancer. Expert Systems with Applications, 2023, 216: 119452.
46. Wang P, Chung A C S. DoubleU-Net: colorectal cancer diagnosis and gland instance segmentation with text-guided feature control//Proceedings of European Conference on Computer Vision–ECCV 2020 Workshops, Glasgow, UK: Springer, 2020: 338-354.
47. Zhang Y D, Liu H Y, Hu Q. TransFuse: fusing transformers and CNNs for medical image segmentation//Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Strasbourg, France: Springer, 2021: 14-24.
48. Zhang H, Yang X, Li D, et al. Dual parallel net: a novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian mixture prior. Journal of Biomedical Informatics, 2023, 139: 104304.
49. Akilandeswari A, Sungeetha D, Joseph C, et al. Automatic detection and segmentation of colorectal cancer with deep residual convolutional neural network. Evidence-Based Complementary and Alternative Medicine, 2022, 2022: 3415603.
50. González-Bueno Puyal J, Brandao P, Ahmad O F, et al. Polyp detection on video colonoscopy using a hybrid 2D/3D CNN. Medical Image Analysis, 2022, 82: 102625.
51. Zheng S, Lin X, Zhang W, et al. MDCC-Net: multiscale double-channel convolution U-Net framework for colorectal tumor segmentation. Computers in Biology and Medicine, 2021, 130: 104183.
52. Li D G, Chu X, Cui Y, et al. Improved U-Net based on contour prediction for efficient segmentation of rectal cancer. Computer Methods and Programs in Biomedicine, 2022, 213: 106493.
53. Yue G, Zhuo G, Yan W, et al. Boundary uncertainty aware network for automated polyp segmentation. Neural Networks, 2024, 170: 390-404.
54. Yeung M, Sala E, Schönlieb C B, et al. Focus U-Net: a novel dual attention-gated CNN for polyp segmentation during colonoscopy. Computers in Biology and Medicine, 2021, 137: 104815.
55. Lu Jiaqi, Liu Ruiqing, Zhang Yuejuan, et al. Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence. Intelligent Medicine, 2022, 2(2): 82-87.
56. Kusters K C, Scheeve T, Dehghani N, et al. Colorectal polyp classification using confidence-calibrated convolutional neural networks//Proceedings of Medical Imaging 2022: Computer-Aided Diagnosis, SPIE, 2022, 12033: 442-454.
57. Paladini E, Vantaggiato E, Bougourzi F, et al. Two ensemble-CNN approaches for colorectal cancer tissue type classification. Journal of Imaging, 2021, 7(3): 51.

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