Abstract
Neuroimaging has been widely used in computer-aided clinical diagnosis and treatment, and the rapid increase of neuroimage repositories introduces great challenges for efficient neuroimage search. Existing image search methods often use triplet loss to capture high-order relationships between samples. However, we find that the traditional triplet loss is difficult to pull positive and negative sample pairs to make their Hamming distance discrepancies larger than a small fixed value. This may reduce the discriminative ability of learned hash code and degrade the performance of image search. To address this issue, in this work, we propose a deep disentangled momentum hashing (DDMH) framework for neuroimage search. Specifically, we first investigate the original triplet loss and find that this loss function can be determined by the inner product of hash code pairs. Accordingly, we disentangle hash code norms and hash code directions and analyze the role of each part. By decoupling the loss function from the hash code norm, we propose a unique disentangled triplet loss, which can effectively push positive and negative sample pairs by desired Hamming distance discrepancies for hash codes with different lengths. We further develop a momentum triplet strategy to address the problem of insufficient triplet samples caused by small batch-size for 3D neuroimages. With the proposed disentangled triplet loss and the momentum triplet strategy, we design an end-to-end trainable deep hashing framework for neuroimage search. Comprehensive empirical evidence on three neuroimage datasets shows that DDMH has better performance in neuroimage search compared to several state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Graham, R.N., Perriss, R., Scarsbrook, A.F.: DICOM demystified: a review of digital file formats and their use in radiological practice. Clin. Radiol. 60(11), 1133–1140 (2005)
Grimson, W.E.L., Kikinis, R., Jolesz, F.A., Black, P.: Image-guided surgery. Sci. Am. 280(6), 54–61 (1999)
Owais, M., Arsalan, M., Choi, J., Park, K.R.: Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. J. Clin. Med. 8(4), 462 (2019)
Cheng, B., Liu, M., Shen, D., Li, Z., Zhang, D.: Multi-domain transfer learning for early diagnosis of Alzheimer’s disease. Neuroinformatics 15(2), 115–132 (2017)
Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157–168 (2018)
Holt, A., Bichindaritz, I., Schmidt, R., Perner, P.: Medical applications in case-based reasoning. Knowl. Eng. Rev. 20(3), 289–292 (2005)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: ICCV, pp. 2130–2137 (2009)
Yang, E., Deng, C., Liu, W., Liu, X., Tao, D., Gao, X.: Pairwise relationship guided deep hashing for cross-modal retrieval. In: AAAI, pp. 1618–1625 (2017)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1092–1104 (2012)
Dai, B., Guo, R., Kumar, S., He, N., Song, L.: Stochastic generative hashing. arXiv preprint arXiv:1701.02815 (2017)
Yang, E., Deng, C., Liu, T., Liu, W., Tao, D.: Semantic structure-based unsupervised deep hashing. IJCA I, 1064–1070 (2018)
Liu, W., Wang, J., Ji, R., Jiang, Y., Chang, S.F.: Supervised hashing with kernels. In: CVPR, pp. 2074–2081 (2012)
Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T.: Fast supervised discrete hashing. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 490–496 (2017)
Yang, E., Deng, C., Li, C., Liu, W., Li, J., Tao, D.: Shared predictive cross-modal deep quantization. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5292–5303 (2018)
Cao, Y., Long, M., Liu, B., Wang, J., KLiss, M.: Deep Cauchy hashing for hamming space retrieval. In: CVPR, pp. 1229–1237 (2018)
Cao, Y., Liu, B., Long, M., Wang, J., KLiss, M.: HashGAN: deep learning to hash with pair conditional Wasserstein GAN. In: CVPR, pp. 1287–1296 (2018)
Zhang, R., Lin, L., Zhang, R., Zuo, W., Zhang, L.: Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans. Image Process. 24(12), 4766–4779 (2015)
Deng, C., Yang, E., Liu, T., Li, J., Liu, W., Tao, D.: Unsupervised semantic-preserving adversarial hashing for image search. IEEE Trans. Image Process. 28(8), 4032–4044 (2019)
Deng, C., Chen, Z., Liu, X., Gao, X., Tao, D.: Triplet-based deep hashing network for cross-modal retrieval. IEEE Trans. Image Process. 27(8), 3893–3903 (2018)
Chen, L., Honeine, P., Qu, H., Zhao, J., Sun, X.: Correntropy-based robust multilayer extreme learning machines. Pattern Recogn. 84, 357–370 (2018)
Chen, L., Qu, H., Zhao, J., Chen, B., Principe, J.C.: Efficient and robust deep learning with correntropy-induced loss function. Neural Comput. Appl. 27(4), 1019–1031 (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690–4699 (2019)
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: CVPR, pp. 5265–5274 (2018)
Chen, Z., Cai, R., Lu, J., Feng, J., Zhou, J.: Order-sensitive deep hashing for multimorbidity medical image retrieval. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-Lopez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_70
Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: NeurIPS,pp. 2482–2491 (2017)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. arXiv preprint arXiv:1911.05722 (2019)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR (2014)
Jack Jr, C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging Offic. J. Int. Soc. Magn. Reson. Med. 27(4), 685–691 (2008)
Ellis, K.A., et al.: The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int. Psychogeriatr. 21(4), 672–687 (2009)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NeurIPS, pp. 1753–1760 (2009)
Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: Spherical hashing. In: CVPR, pp. 2957–2964 (2012)
Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)
Jin, Z., Li, C., Lin, Y., Cai, D.: Density sensitive hashing. IEEE Trans. Cybern. 44(8), 1362–1371 (2014)
Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels. IJCA I, 1711–1717 (2016)
Yang, H.F., Lin, K., Chen, C.S.: Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 437–451 (2017)
Acknowledgments
This work was partly supported by NIH grants (Nos. AG041721, AG053867).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, E. et al. (2020). Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-59710-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59709-2
Online ISBN: 978-3-030-59710-8
eBook Packages: Computer ScienceComputer Science (R0)
