Abstract
Magnetic resonance imaging (MRI) and positron emission tomography (PET) are widely used in diagnosis of Alzheimer’s disease (AD). In practice, incomplete modality problem is unavoidable due to the cost of data acquisition. Deep learning based models especially generative adversarial networks (GAN) are usually adopted to impute missing images. However, there are still some problems: (1) there are many regions unrelated to the disease and have little significance in the actual diagnosis in brain images, which are very cumbersome to generate. (2) The image generated by GAN would introduce noises causing the poor performance in the diagnostic model. To address these problems, a pairwise feature-based generation adversarial network is proposed. Specifically, features from the original brain images are extracted firstly. For the paired data without modality loss, the extracted MRI features are used as input to generate its corresponding PET features, which not only reduces the scale of the model, but also ensures the direct correlation between the generated features and the diagnosis. In addition, the available real PET features of the paired samples are added as label to constrain the generated ones. Finally, the attention mechanism is adopted in both the generator and discriminator, which can effectively retain the structural information of the feature itself. A large number of experiments have demonstrated that our proposed method has achieved promising results in the diagnosis of AD.
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References
Weller, J., Budson, A.: Current understanding of Alzheimer’s disease diagnosis and treatment. F1000Res 7, 1161 (2018)
Kirova, A.-M., Bays, R.B., Lagalwar, S.: Working memory and executive function decline across normal aging, mild cognitive impairment, and Alzheimer’s disease. Biomed. Res. Int. 2015, 1–9 (2015)
Guarino, A., Favieri, F., Boncompagni, I., Agostini, F., Cantone, M., Casagrande, M.: Executive functions in Alzheimer disease: a systematic review. Front. Aging Neurosci. 10, 437 (2019)
Kitamura, Y., Usami, R., Ichihara, S., Kida, H., Satoh, M., Tomimoto, H., Murata, M., Oikawa, S.: Plasma protein profiling for potential biomarkers in the early diagnosis of Alzheimer’s disease. Neurol. Res. 39(3), 231–238 (2017)
Cummings, J., Lee, G., Ritter, A., Sabbagh, M., Zhong, K.: “Alzheimer’s disease drug development pipeline: 2019”, Alzheimer’s \& Dement. Transl. Res. Clin. Interv. 5, 272–293 (2019)
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D., Alzheimer's Disease Neuroimaging Initiative et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment, Neuroimage, 55(3), 856–867, (2011)
Lin, E., Lin, C.-H., Lane, H.-Y.: Deep learning with neuroimaging and genomics in Alzheimer’s disease. Int. J. Mol. Sci. 22(15), 7911 (2021)
Zhang, D., Shen, D., Alzheimer's Disease Neuroimaging Initiative et al.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage, 59(2): 895–907, (2012).
Ieracitano, C., Mammone, N., Hussain, A., Morabito, F.C.: A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw. 123, 176–190 (2020)
Zhou, T., Thung, K.-H., Liu, M., Shi, F., Zhang, C., Shen, D.: Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Med. Image Anal. 60, 101630 (2020)
Basavegowda, H.S., Dagnew, G.: Deep learning approach for microarray cancer data classification. CAAI Trans. Intell. Technol. 5(1), 22–33 (2020)
Shao, W., He, L., Philip, S. Y.: Multiple incomplete views clustering via weighted nonnegative matrix factorization with $$ L_ {2, 1} $$ regularization, In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 318–334 (2015)
Wen, J., Zhang, Z., Xu, Y., Zhang, B., Fei, L., Liu, H.: Unified embedding alignment with missing views inferring for incomplete multi-view clustering. Proc. AAAI Conf. Artif. Intell. 33(01), 5393–5400 (2019)
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks, (2014) arXiv Prepr. arXiv: 1406.2661.
Zhu, C., Yan, W., Cai, X., Liu, S., Li, T.H., Li, G.: Neural saliency algorithm guide bi-directional visual perception style transfer. CAAI Trans. Intell. Technol. 5(1), 1–8 (2020)
Pan, Y., Liu, M., Lian, C., Zhou, T., Xia, Y., Shen,D.: Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer’s disease diagnosis, In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 455–463 (2018).
Pan, Y., Liu, M., Lian, C., Xia, Y., Shen, D.: Disease-image specific generative adversarial network for brain disease diagnosis with incomplete multi-modal neuroimages, In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 137–145 (2019).
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. Adv. Neural Inf. Process. Syst. 29, 2234–2242 (2016)
Rensink, R.A.: The dynamic representation of scenes. Vis. cogn. 7(1–3), 17–42 (2000)
Yang, J., Xing, D., Hu, Z., Yao, T.: A two-branch network with pyramid-based local and spatial attention global feature learning for vehicle re-identification. CAAI Trans. Intell. Technol. 6(1), 46–54 (2021)
Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for GANs do actually converge? In: International Conference on Machine Learning, pp. 3481–3490 (2018).
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)
Ashburner, J., Friston, K.J.: Voxel-based morphometry—the methods. Neuroimage 11(6), 805–821 (2000)
Li, S.-Y., Jiang, Y., Zhou, Z.-H.: Partial multi-view clustering, In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28(1) (2014)
Yoon, J., Jordon, J., Van Der Schaar, M.: GAIN: Missing data imputation using generative adversarial nets, In: 35th International Conference Machine Learning. ICML 2018, vol. 13, pp. 9042–9051, (2018).
Funding
This work was supported in part by National Natural Science Foundation of China (Nos. 62076129, 61501230, 61732006, 61876082 and 61861130366), National Science and Technology Major Project (No. 2018ZX10201002), and the National Key R&D Program of China (Grant Nos.: 2018YFC2001600, 2018YFC2001602).
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QZ and DZ received the idea, identified and coordinated the study. HY partially designed the methods. QZ contributed to the method. HY contributed to programing generated results, and wrote the initial draft. QZ, YY, YJ and DZ revised the manuscript. All authors read, revised and approved the final manuscript.
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Ye, H., Zhu, Q., Yao, Y. et al. Pairwise feature-based generative adversarial network for incomplete multi-modal Alzheimer’s disease diagnosis. Vis Comput 39, 2235–2244 (2023). https://doi.org/10.1007/s00371-021-02354-5
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DOI: https://doi.org/10.1007/s00371-021-02354-5