Abstract
Robustness of medical image classification models is limited by its exposure to the candidate disease classes. Generalized zero shot learning (GZSL) aims at correctly predicting seen and unseen classes and most current GZSL approaches have focused on the single label case. It is common for chest x-rays to be labelled with multiple disease classes. We propose a novel multi-label GZSL approach using: 1) class specific feature disentanglement and 2) semantic relationship between disease labels distilled from BERT models pre-trained on biomedical literature. We learn a dictionary from distilled text embeddings, and leverage them to synthesize feature vectors that are representative of multi-label samples. Compared to existing methods, our approach does not require class attribute vectors, which are an essential part of GZSL methods for natural images but are not available for medical images. Our approach outperforms state of the art GZSL methods for chest xray images.
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
Similar content being viewed by others
References
Bian, C., Yuan, C., Ma, K., Yu, S., Wei, D., Zheng, Y.: Domain adaptation meets zero-shot learning: an annotation-efficient approach to multi-modality medical image segmentation. IEEE Trans. Med. Imaging 41(5), 1043–1056 (2022)
Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vayá, M.: PadChest: A large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 9912–9924. Curran Associates, Inc. (2020). https://proceedings.neurips.cc/paper/2020/file/70feb62b69f16e0238f741fab228fec2-Paper.pdf
Chen, Y., et al.: Zero-shot medical image artifact reduction. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 862–866 (2020). https://doi.org/10.1109/ISBI45749.2020.9098566
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint: arXiv:1810.04805 (2018)
Feng, Y., Huang, X., Yang, P., Yu, J., Sang, J.: Non-generative generalized zero-shot learning via task-correlated disentanglement and controllable samples synthesis. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9336–9345 (2022)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016). https://doi.org/10.1001/jama.2016.17216
Hayat, N., Lashen, H., Shamout, F.: Multi-label generalized zero shot learning for the classification of disease in chest radiographs. In: Proceeding of the Machine Learning for Healthcare Conference, pp. 461–477 (2021)
Huynh, D., Elhamifar, E.: A shared multi-attention framework for multi-label zero-shot learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8773–8783 (2020). https://doi.org/10.1109/CVPR42600.2020.00880
Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. arXiv preprint: arXiv:1901.07031 (2017)
Jia, J., He, F., Gao, N., Chen, X., Huang, K.: Learning disentangled label representations for multi-label classification (2022). https://doi.org/10.48550/arXiv.2212.01461
Kong, X., et al.: En-compactness: self-distillation embedding and contrastive generation for generalized zero-shot learning. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9296–9305 (2022). https://doi.org/10.1109/CVPR52688.2022.00909
Kori, A., Krishnamurthi, G.: Zero shot learning for multi-modal real time image registration. arXiv preprint: arXiv:1908.06213 (2019)
Lee, C.W., Fang, W., Yeh, C.K., Wang, Y.C.F.: Multi-label zero-shot learning with structured knowledge graphs. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1576–1585 (2018). https://doi.org/10.1109/CVPR.2018.00170
Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2020)
Mahapatra, D., Bozorgtabar, B., Kuanar, S., Ge, Z.: Self-supervised multimodal generalized zero shot learning for Gleason grading. In: Albarqouni, S., et al. (eds.) DART/FAIR -2021. LNCS, vol. 12968, pp. 46–56. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87722-4_5
Mahapatra, D., Bozorgtabar, B., Ge, Z.: Medical image classification using generalized zero shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 3344–3353 (2021)
Mahapatra, D., Ge, Z., Reyes, M.: Self-supervised generalized zero shot learning for medical image classification using novel interpretable saliency maps. IEEE Trans. Med. Imaging 41(9), 2443–2456 (2022). https://doi.org/10.1109/TMI.2022.3163232
Mensink, T., Gavves, E., Snoek, C.G.: COSTA: co-occurrence statistics for zero-shot classification. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2441–2448 (2014). https://doi.org/10.1109/CVPR.2014.313
Park, T., et al.: Swapping autoencoder for deep image manipulation. In: Advances in Neural Information Processing Systems (2020)
Paul, A., et al.: Generalized zero-shot chest x-ray diagnosis through trait-guided multi-view semantic embedding with self-training. IEEE Trans. Med. Imaging 40, 2642–2655 (2021). https://doi.org/10.1109/TMI.2021.3054817
Su, H., Li, J., Chen, Z., Zhu, L., Lu, K.: Distinguishing unseen from seen for generalized zero-shot learning. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7875–7884 (2022). https://doi.org/10.1109/CVPR52688.2022.00773
Verma, V., Arora, G., Mishra, A., Rai, P.: Generalized zero-shot learning via synthesized examples. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4281–4289 (2018)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the CVPR (2017)
Wu, J., Zhang, T., Zha, Z.J., Luo, J., Zhang, Y., Wu, F.: Self-supervised domain-aware generative network for generalized zero-shot learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12767–12776 (2020)
Xian, Y., Sharma, S., Schiele, B., Akata, Z.: F-VAEGAN-D2: a feature generating framework for any-shot learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10275–10284 (2019)
Yuan, Z., Yan, Y., Sonka, M., Yang, T.: Large-scale robust deep AUC maximization: A new surrogate loss and empirical studies on medical image classification. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3020–3029 (2021)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=r1Ddp1-Rb
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mahapatra, D. et al. (2023). Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-43895-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43894-3
Online ISBN: 978-3-031-43895-0
eBook Packages: Computer ScienceComputer Science (R0)
