Abstract
Accurately classifying the histological subtype of non-small cell lung cancer (NSCLC) using computed tomography (CT) images is critical for clinicians in determining the best treatment options for patients. Although recent advances in multi-view approaches have shown promising results, discrepancies between CT images from different views introduce various representations in the feature space, hindering the effective integration of multiple views and thus impeding classification performance. To solve this problem, we propose a novel method called cross-aligned representation learning (CARL) to learn both view-invariant and view-specific representations for more accurate NSCLC histological subtype classification. Specifically, we introduce a cross-view representation alignment learning network which learns effective view-invariant representations in a common subspace to reduce multi-view discrepancies in a discriminability-enforcing way. Additionally, CARL learns view-specific representations as a complement to provide a holistic and disentangled perspective of the multi-view CT images. Experimental results demonstrate that CARL can effectively reduce the multi-view discrepancies and outperform other state-of-the-art NSCLC histological subtype classification methods.
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References
Aerts, H.J.W.L. et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative Radiomics approach. Nat. Commun. 5(1), 4006 (2014). https://doi.org/10.1038/ncomms5006
Bakr, S. et al.: A radiogenomic dataset of non-small cell lung cancer. Sci. Data 5(1), 180202 (2018). https://doi.org/10.1038/sdata.2018.202
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Chaunzwa, T.L. et al.: Deep learning classification of lung cancer histology using CT images. Sci. Rep. 11(1), 5471 (2021). https://doi.org/10.1038/s41598-021-84630-x
Clark, K., et al.: The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J. Digit Imaging. 26(6), 1045–1057 (2013). https://doi.org/10.1007/s10278-013-9622-7
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
Dou, Q., et al.: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. 64(7), 1558–1567 (2017). https://doi.org/10.1109/TBME.2016.2613502
Feng, Y., et al.: GVCNN: Group-view convolutional neural networks for 3D shape recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 264–272 (2018). https://doi.org/10.1109/CVPR.2018.00035
Guo, Y., et al.: Histological subtypes classification of lung cancers on CT images using 3D deep learning and radiomics. Acad. Radiol. 28(9), e258–e266 (2021). https://doi.org/10.1016/j.acra.2020.06.010
He, K., et al.: Deep Residual Learning for Image Recognition. http://arxiv.org/abs/1512.03385 (2015). https://doi.org/10.48550/arXiv.1512.03385
Li, C., et al.: Multi-view mammographic density classification by dilated and attention-guided residual learning. IEEE/ACM Trans. Comput. Biol. Bioinf. 18(3), 1003–1013 (2021). https://doi.org/10.1109/TCBB.2020.2970713
Li, S., et al.: Adaptive multimodal fusion with attention guided deep supervision net for grading hepatocellular carcinoma. IEEE J. Biomed. Health Inform. 26(8), 4123–4131 (2022). https://doi.org/10.1109/JBHI.2022.3161466
Marentakis, P., et al.: Lung cancer histology classification from CT images based on radiomics and deep learning models. Med. Biol. Eng. Comput. 59(1), 215–226 (2021). https://doi.org/10.1007/s11517-020-02302-w
Meng, Z., et al.: MSMFN: an ultrasound based multi-step modality fusion network for identifying the histologic subtypes of metastatic cervical lymphadenopathy. In: IEEE Transactions on Medical Imaging, pp. 1–1 (2022). https://doi.org/10.1109/TMI.2022.3222541
Pereira, T. et al.: Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images. J. Clin. Med. 10(1), 118 (2021). https://doi.org/10.3390/jcm10010118
Sahu, P., et al.: A lightweight multi-section CNN for lung nodule classification and malignancy estimation. IEEE J. Biomed. Health Inform. 23(3), 960–968 (2019). https://doi.org/10.1109/JBHI.2018.2879834
Sedrez, J.A., et al.: Non-invasive postural assessment of the spine in the sagittal plane: a systematic review. Motricidade 12(2), 140–154 (2016). https://doi.org/10.6063/motricidade.6470
Su, H., et al.: Multi-view convolutional neural networks for 3D shape recognition. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 945–953 (2015). https://doi.org/10.1109/ICCV.2015.114
Su, R., et al.: Identification of expression signatures for non-small-cell lung carcinoma subtype classification. Bioinformatics 36(2), 339–346 (2019). https://doi.org/10.1093/bioinformatics/btz557
Tomassini, S., et al.: Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: a survey. Comput. Biol. Med. 146, 105691 (2022). https://doi.org/10.1016/j.compbiomed.2022.105691
Wang, J., et al.: UASSR: unsupervised arbitrary scale super-resolution reconstruction of single anisotropic 3D images via disentangled representation learning. In: Wang, L. et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, pp. 453–462 Springer Nature Switzerland, Cham (2022). https://doi.org/10.1007/978-3-031-16446-0_43
Wu, X., et al.: Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. Eur. J. Radiol. 128, 109041 (2020). https://doi.org/10.1016/j.ejrad.2020.109041
Yanagawa, M., et al.: Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network. Eur. Radiol. 31(4), 1978–1986 (2021). https://doi.org/10.1007/s00330-020-07339-x
Zellinger, W., et al.: Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning. http://arxiv.org/abs/1702.08811 (2019)
Zhang, N., et al.: Circular RNA circSATB2 promotes progression of non-small cell lung cancer cells. Mol. Cancer. 19(1), 101 (2020). https://doi.org/10.1186/s12943-020-01221-6
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61971393, 62272325, 61871361 and 61571414.
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Luo, Y. et al. (2023). CARL: Cross-Aligned Representation Learning for Multi-view Lung Cancer Histology Classification. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_35
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