The deep learning method automatically extracts advanced features from a large amount of data, avoiding cumbersome manual feature screening, and using digital pathology and artificial intelligence technology to build a computer-aided diagnosis system to help pathologists quickly make objective and reliable diagnoses and improve work efficiency. Because pathological images are limited by factors such as sample size, manual labeling expertise, and complexity, artificial intelligence algorithms have not been extensively and in-depth researched on pathological images of lung cancer metastasis. Therefore, this paper proposes a lung cancer metastasis segmentation method based on pathological images, to further improve the computer-aided diagnosis method of lung cancer.
References
[1]
Zhang, C.X., Wu, W.J., Yang, J. and Sun, J.Y. (2022) Application of Artificial Intelligence in Respiratory Medicine. Journal of Digital Health, 1, 30-39.
https://doi.org/10.55976/jdh.1202215330-39
[2]
Fan, L., Xia, Z.Q., Zhang, X.B. and Feng, X.Y. (2017) Lung Nodule Detection Based on 3D Convolutional Neural Networks. 2017 International Conference on the Frontiers and Advances in Data Science, Xi’an, 23-25 October 2017, 7-10.
[3]
Van Gansbeke, W., Vandenhende, S., Georgoulis, S. and Van Gool, L. (2021) Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, 10-17 October 2021, 10032-10042. https://doi.org/10.1109/ICCV48922.2021.00990
[4]
Wang, D.Y., Khosla, A., Gargeya, R., Irshad, H. and Beck, A.H. (2016) Deep Learning for Identifying Metastatic Breast Cancer. arXiv.
[5]
Weng, W.H. and Zhu, X. (2021) INet: Convolutional Networks for Biomedical Image Segmentation. IEEE Access, 9, 16591-16603.
https://doi.org/10.1109/ACCESS.2021.3053408
[6]
Lassen, B.C., Jacobs, C., Kuhnigk, J.M., van Ginneken, B. and van Rikxoort, E.M. (2015) Robust Semi-Automatic Segmentation of Pulmonary Subsolid Nodules in Chest Computed Tomography Scans. Physics in Medicine & Biology, 60, Article 1307. https://doi.org/10.1088/0031-9155/60/3/1307
[7]
Long, J., Shelhamer, E. and Darrell, T. (2015) Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, 7-12 June 2015, 3431-3440.
https://doi.org/10.1109/CVPR.2015.7298965
[8]
Chen, L.C., Zhu, Y.K., Papandreou, G., Schroff, F. and Adam, H. (2018) Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C. and Weiss, Y., Eds., Computer Vision—ECCV 2018, Springer, Cham, 833-851.
https://doi.org/10.1007/978-3-030-01234-2_49
[9]
Badrinarayanan, V., Kendall, A. and Cipolla, R. (2017) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495.
https://doi.org/10.1109/TPAMI.2016.2644615
[10]
He, K.M., Gkioxari, G., Dollár, P. and Girshick, R. (2017) Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, 22-29 October 2017, 2980-2988. https://doi.org/10.1109/ICCV.2017.322
[11]
Isensee, F., Petersen, J., Kohl, S.A.A., Jager, P.F. and Maier-Hein, K.H. (1904) nnU-Net: Breaking the Spell on Successful Medical Image Segmentation. arXiv.
[12]
Azad, R., Asadi-Aghbolaghi, M., Fathy, M. and Escalera, S. (2019) Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions. Proceedings of the IEEE/ CVF International Conference on Computer Vision Workshops, Seoul, 27-28 October 2019, 406-415. https://doi.org/10.1109/ICCVW.2019.00052
[13]
Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I. and Patel, V.M. (2021) Medical Transformer: Gated Axial-Attention for Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, 27 September 2021, 36-46. https://doi.org/10.1007/978-3-030-87193-2_4
[14]
Chen, J., Lu, Y., Yu, Q., et al. (2021) Transunet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv.
[15]
Lou, A., Guan, S.Y., Ko, H. and Loew, M.H. (2021) Caranet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects. arXiv.
