Abstract
The limitations of domain dependence in neural networks and data scarcity are addressed in this paper by analyzing the problem of semi-supervised medical image classification across multiple visual domains using a single integrated framework. Under this premise, we learn a universal parametric family of neural networks, which share a majority of their weights across domains by learning a few adaptive domain-specific parameters. We train these universal networks on a suitable pretext task that captures a meaningful representation for image classification and further finetune the networks using a small fraction of training data. We perform our experiments on five medical datasets spanning breast, cervical, and colorectal cancer. Extensive experiments on architectures of domain-adaptive parameters demonstrate that our data-deficient universal model performs equivalently to a fully supervised setup, rendering a semi-supervised multi-domain setting with lower numbers of training samples for medical data extremely feasible in the real world.
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References
Bai, W., et al.: Self-supervised learning for cardiac MR image segmentation by anatomical position prediction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 541–549. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_60
Brosch, T., Tam, R.: Manifold learning of brain MRIs by deep learning. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 633–640. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_78
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations In: Proceedings of the 34th International Conference on Neural Information Processing Systems-NIPS 2020, pp. 2546–12558 (2020)
Cheerla, A., Gevaert, O.: Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14), i446–i454 (2019)
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (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 (2016)
Chhipa, P.C., Upadhyay, R., Grund Pihlgren, G., Saini, R., Uchida, S., Liwicki, M.: Magnification prior: a self-supervised method for learning representations on breast cancer histopathological images. arXiv e-prints arXiv:2203.07707, March 2022
Feng, Y., Liu, Y., Luo, J.: Universal model for multi-domain medical image retrieval (2020). https://doi.org/10.48550/arxiv.2007.08628
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: 6th International Conference on Learning Representations, ICLR 2018 (2018). https://openreview.net/forum?id=S1v4N2l0-
Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi-supervised learning for image classification. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 902–909. IEEE (2010)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316 (2016). https://doi.org/10.1001/jama.2016.17216
Hajiramezanali, E., Zamani Dadaneh, S., Karbalayghareh, A., Zhou, M., Qian, X.: Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, p. 9133–9142. Curran Associates, Inc. (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Hussain, E., Mahanta, L.B., Borah, H., Das, C.R.: Liquid based-cytology pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions. Data Brief 30, 105589 (2020)
Imran, A.A.Z., et al.: Self-supervised, semi-supervised, multi-context learning for the combined classification and segmentation of medical images (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13815–13816 (2020). https://doi.org/10.1609/aaai.v34i10.7179
Jamaludin, A., Kadir, T., Zisserman, A.: Self-supervised learning for spinal MRIs. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS 2017. LNCS, vol. 10553, pp. 294–302. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_34
Janowczyk, A., Basavanhally, A., Madabhushi, A.: Stain normalization using sparse autoencoders (StaNoSA): application to digital pathology. Comput. Med. Imaging Graph. 57, 50–61 (2017). Recent Developments in Machine Learning for Medical Imaging Applications
Jantzen, J., Norup, J., Dounias, G., Bjerregaard, B.: Pap-smear benchmark data for pattern classification. In: Nature inspired Smart Information Systems: EU Co-ordination Action, NiSIS 2005, 01 January 2005, pp. 1–9 (2005)
Kather, J.N., et al.: Collection of textures in colorectal cancer histology (2016)
Khosravan, N.: Semi-supervised multi-task learning for lung cancer diagnosis, vol. 2018, pp. 710–713 (2018). https://doi.org/10.1109/EMBC.2018.8512294
Kraljevic, Z., et al..: Multi-domain clinical natural language processing with MedCAT: the medical concept annotation toolkit. Artif. Intell. Med. 117, 102083 (2021)
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017 (2017)
Li, H., Wang, Y., Wan, R., Wang, S., Li, T.Q., Kot, A.: Domain generalization for medical imaging classification with linear-dependency regularization, vol. 33, pp. 3118–3129 (2020)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29(2), 102–127 (2019). Special Issue: Deep Learning in Medical Physics
Mitra, S., Das, N., Dey, S., Chakraborty, S., Nasipuri, M., Naskar, M.K.: Cytology image analysis techniques toward automation: systematically revisited. ACM Comput. Surv. 54(3) (2021).https://doi.org/10.1145/3447238
Mojab, N., et al.: Real-world multi-domain data applications for generalizations to clinical settings. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 677–684 (2020). https://doi.org/10.1109/ICMLA51294.2020.00112
Plissiti, M.E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., Charchanti, A.: SIPaKMeD: a new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3144–3148 (2018). https://doi.org/10.1109/ICIP.2018.8451588
Rebuffi, S.A., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. In: Guyon, I., et al.. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 1–11. Curran Associates, Inc. (2017)
Rebuffi, S.A., Ehrhardt, S., Han, K., Vedaldi, A., Zisserman, A.: Semi-supervised learning with scarce annotations. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3294–3302 (2020)
Rebuffi, S.A., Vedaldi, A., Bilen, H.: Efficient parametrization of multi-domain deep neural networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8119–8127 (2018). https://doi.org/10.1109/CVPR.2018.00847
Santosh, K., Das, N., Ghosh, S.: Cytology image analysis. In: Santosh, K., Das, N., Ghosh, S. (eds.) Deep Learning Models for Medical Imaging, Chap. 4. Primers in Biomedical Imaging Devices and Systems, pp. 99–123. Academic Press (2022)
Schmarje, L., Santarossa, M., Schröder, S.M., Koch, R.: A survey on semi-, self-and unsupervised learning for image classification. arXiv:2002.08721 (2020)
Sen, A., Mitra, S., Chakraborty, S., Mondal, D., Santosh, K., Das, N.: Ensemble framework for unsupervised cervical cell segmentation. In: 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), pp. 345–350 (2022). https://doi.org/10.1109/CBMS55023.2022.00068
Shen, L., et al.: Multi-domain image completion for random missing input data. IEEE Trans. Med. Imaging 40(4), 1113–1122 (2021)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567 (2016)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems 30, pp. 1195–1204 (2017)
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., Nandi, A.K.: Medical image segmentation using deep learning: a survey. IET Image Process. 16(5), 1243–1267 (2022)
Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., Fujita, H.: Three-dimensional CT image segmentation by combining 2D fully convolutional network with 3D majority voting. In: Carneiro, G., et al. (eds.) LABELS/DLMIA 2016. LNCS, vol. 10008, pp. 111–120. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_12
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Chavhan, R., Banerjee, B., Das, N. (2023). Semi-supervised Multi-domain Learning for Medical Image Classification. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_3
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