{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:09:46Z","timestamp":1743116986144,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031720857"},{"type":"electronic","value":"9783031720864"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72086-4_54","type":"book-chapter","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:34:45Z","timestamp":1727987685000},"page":"575-584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RDD-Net: Randomized Joint Data-Feature Augmentation and Deep-Shallow Feature Fusion Networks for Automated Diagnosis of Glaucoma"],"prefix":"10.1007","author":[{"given":"Yilin","family":"Tang","sequence":"first","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"54_CR1","unstructured":"Gaibullaeva, N. N.: The role of clinical examination in early diagnosis of glaucoma. In: Health and Medical Sciences 4(3), 333\u2013337 (2021)"},{"key":"54_CR2","doi-asserted-by":"crossref","unstructured":"Deperlioglu, O., et al.: Explainable framework for glaucoma diagnosis by image processing and convolutional neural network synergy: Analysis with doctor evaluation. In: Future Generation Computer Systems 129, 152\u2013169 (2022)","DOI":"10.1016\/j.future.2021.11.018"},{"key":"54_CR3","doi-asserted-by":"crossref","unstructured":"Vali, M., et al.: Differentiating glaucomatous optic neuropathy from non-glaucomatous optic neuropathies using deep learning algorithms. In: American Journal of Ophthalmology, 252, 1\u20138 (2023)","DOI":"10.1016\/j.ajo.2023.02.016"},{"key":"54_CR4","doi-asserted-by":"crossref","unstructured":"Ghorui, A., et al.: Deployment of CNN on color fundus images for the automatic detection of glaucoma. In: International Journal of Applied Science and Engineering 20(1), 1\u20139 (2023)","DOI":"10.6703\/IJASE.202303_20(1).003"},{"key":"54_CR5","doi-asserted-by":"crossref","unstructured":"Velpula, V. K., et al.: Automatic glaucoma detection from fundus images using deep convolutional neural networks and exploring networks behaviour using visualization techniques. In: SN Computer Science 4(5), 487 (2023)","DOI":"10.1007\/s42979-023-01945-4"},{"key":"54_CR6","doi-asserted-by":"crossref","unstructured":"Shoukat, A., et al.: Automatic diagnosis of glaucoma from retinal images using deep learning approach. In: Diagnostics 13(10), 1738 (2023)","DOI":"10.3390\/diagnostics13101738"},{"key":"54_CR7","doi-asserted-by":"crossref","unstructured":"Guo, J. M., et al.: A study of the interpretability of fundus analysis with deep learning-based approaches for glaucoma assessment. In: Electronics 12(9), 2013 (2023)","DOI":"10.3390\/electronics12092013"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"Saha, S., et al.: A fast and fully automated system for glaucoma detection using color fundus photographs. In: Scientific Reports13(1), 18408(2023)","DOI":"10.1038\/s41598-023-44473-0"},{"key":"54_CR9","doi-asserted-by":"crossref","unstructured":"Thanki, R.: A deep neural network and machine learning approach for retinal fundus image classification. In: Healthcare Analytics 3, 100140 (2023)","DOI":"10.1016\/j.health.2023.100140"},{"key":"54_CR10","doi-asserted-by":"crossref","unstructured":"Li, T., et al.: Applications of deep learning in fundus images: A review. In: Medical Image Analysis 69, 101971 (2021)","DOI":"10.1016\/j.media.2021.101971"},{"key":"54_CR11","doi-asserted-by":"crossref","unstructured":"Zhou, K., et al.: Domain generalization: A survey. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"54_CR12","first-page":"242","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2023, LNCS","author":"H Kim","year":"2023","unstructured":"Kim, H., et al.: DiMix: Disentangle-and-Mix based domain generalizable medical image segmentation. In: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2023, LNCS, vol. 14222, pp. 242\u2013251. Springer, Cham (2023)"},{"key":"54_CR13","first-page":"89","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2023, LNCS","author":"Z Chen","year":"2023","unstructured":"Chen, Z., et al.: Treasure in Distribution: A domain randomization based multi-source domain generalization for 2D medical image segmentation. In: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2023, LNCS, vol. 14223, pp. 89\u201399. Springer, Cham (2023)"},{"key":"54_CR14","doi-asserted-by":"crossref","unstructured":"Ran, G., et al.: CDDSA: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation. In: Medical Image Analysis 89, 102904 (2023)","DOI":"10.1016\/j.media.2023.102904"},{"key":"54_CR15","first-page":"430","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2023, LNCS","author":"H Che","year":"2023","unstructured":"Che, H., et al.: Towards generalizable diabetic retinopathy grading in unseen domains. In: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2023, LNCS, vol. 14224, pp. 430\u2013440. Springer, Cham (2023)"},{"key":"54_CR16","unstructured":"Galappaththige, C. J., et al.: Generalizing to unseen domains in diabetic retinopathy classification. In: Winter Conference on Applications of Computer Vision 2023, pp. 7685\u20137695 (2023)"},{"key":"54_CR17","doi-asserted-by":"crossref","unstructured":"Li, B., et al.: On feature normalization and data augmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021, pp. 12378\u201312387 (2021)","DOI":"10.1109\/CVPR46437.2021.01220"},{"key":"54_CR18","doi-asserted-by":"crossref","unstructured":"Gokhale T, et al.: Improving diversity with adversarially learned transformations for domain generalization. In: Winter Conference on Applications of Computer Vision 2023, pp. 434\u2013443 (2023)","DOI":"10.1109\/WACV56688.2023.00051"},{"key":"54_CR19","doi-asserted-by":"crossref","unstructured":"Lu, J., et al.: Multi-feature fusion for enhancing image similarity learning. In: IEEE Access 7, 167547\u2013167556 (2019)","DOI":"10.1109\/ACCESS.2019.2953078"},{"key":"54_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Resnest: Split-attention networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2022, pp. 2736\u20132746 (2022)","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"54_CR21","doi-asserted-by":"crossref","unstructured":"Woo, S., et al.: Cbam: Convolutional block attention module. In: European Conference on Computer Vision 2018, pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"54_CR22","unstructured":"Fang, H., et al.: REFUGE2 Challenge: A treasure trove for multi-dimension analysis and evaluation in glaucoma screening. In: arXiv preprint arXiv:2202.08994 (2022)"},{"key":"54_CR23","doi-asserted-by":"crossref","unstructured":"Ahn, J. M., et al.: A deep learning model for the detection of both advanced and early glaucoma using fundus photography. In: PloS one 13(11), e0207982 (2018)","DOI":"10.1371\/journal.pone.0207982"},{"key":"54_CR24","unstructured":"Zhuo, Z., et al.: Origa-light: An online retinal fundus image database for glaucoma analysis and research, In: IEEE Eng. in Med. and Bio. Soc. pp. 3065\u20133068 (2010)"},{"key":"54_CR25","doi-asserted-by":"crossref","unstructured":"Fumero, F., et al.: RIM-ONE: An open retinal image database for optic nerve evaluation, In: International symposium on computer-based medical systems\u2013CBMS, pp. 1\u20136 (2011)","DOI":"10.1109\/CBMS.2011.5999143"},{"key":"54_CR26","unstructured":"d\u2019Ascoli, S., et al.: Convit: Improving vision transformers with soft convolutional inductive biases. In: International Conference on Machine Learning, pp. 2286\u20132296 (2021)"},{"key":"54_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: IEEE\/CVF International Conference on Computer Vision 2021, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"54_CR28","doi-asserted-by":"crossref","unstructured":"Fu, H., et al.: Disc-aware ensemble network for glaucoma screening from fundus image. In: IEEE transactions on medical imaging 37(11), 2493\u20132501 (2018)","DOI":"10.1109\/TMI.2018.2837012"},{"key":"54_CR29","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Attention based glaucoma detection: A large-scale database and CNN model. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019, pp. 10571\u201310580 (2019)","DOI":"10.1109\/CVPR.2019.01082"},{"key":"54_CR30","unstructured":"Yi, Z., et al.: Collaborative learning of semi-supervised segmentation and classification for medical images. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019, pp. 2079\u20132088 (2019)"},{"key":"54_CR31","doi-asserted-by":"crossref","unstructured":"Gupta, S., et al.: Mag-net: Multi-task attention guided network for brain tumor segmentation and classification. In: Srirama, S.N., Lin, J.CW., Bhatnagar, R., Agarwal, S., Reddy, P.K. (eds) International Conference on Big Data Analytics 2021, LNCS, vol. 13147, pp. 3\u201315. Springer, Cham (2021)","DOI":"10.1007\/978-3-030-93620-4_1"},{"key":"54_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Multitask learning for segmentation and classification of tumors in 3d automated breast ultrasound images. In: Medical Image Analysis 70, 101918 (2021)","DOI":"10.1016\/j.media.2020.101918"},{"key":"54_CR33","first-page":"731","volume-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2020, LNCS","author":"J Wu","year":"2020","unstructured":"Wu, J., et al.: Leveraging undiagnosed data for glaucoma classification with teacher-student learning. In: Martel, A.L., et al. (eds) Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2020, LNCS, vol. 12261, pp. 731\u2013740. Springer, Cham (2020)"},{"key":"54_CR34","first-page":"677","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022, LNCS","author":"J Wu","year":"2022","unstructured":"Wu, J., et al.: SeATrans: Learning segmentation-assisted diagnosis model via transformer. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022, LNCS, vol. 13432, pp. 677-687 Springer, Cham (2022)"},{"key":"54_CR35","doi-asserted-by":"crossref","unstructured":"Hemelings, R., et al.: A generalizable deep learning regression model for automated glaucoma screening from fundus images. In: NPJ Digital Medicine 6(1), 112 (2023)","DOI":"10.1038\/s41746-023-00857-0"},{"key":"54_CR36","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Domain generalization on medical imaging classification using episodic train-ing with task augmentation. In: Computers in Biology and Medicine 141, 105144 (2022)","DOI":"10.1016\/j.compbiomed.2021.105144"},{"key":"54_CR37","unstructured":"Zhou, K., et al.: Domain generalization with MixStyle. In: International Conference on Learning Representations\u2013ICLR 2021 (2021)"},{"key":"54_CR38","doi-asserted-by":"crossref","unstructured":"He, A., et al.: CabNet: category attention block for imbalanced diabetic retinopathy grading. In: IEEE Trans. Med. Imaging 40(1), 143153 (2020)","DOI":"10.1109\/TMI.2020.3023463"},{"key":"54_CR39","first-page":"635","volume-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2022, LNCS","author":"M Atwany","year":"2022","unstructured":"Atwany, M., et al.: DRGen: domain generalization in diabetic retinopathy classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2022, LNCS, vol. 13432, pp. 635\u2013644. Springer, Cham (2022)"},{"key":"54_CR40","unstructured":"Rame, A., et al.: Fishr: Invariant gradient variances for out-of-distribution generalization. In: International Conference on Machine Learning, pp. 18347\u201318377 (2022)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72086-4_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:41:49Z","timestamp":1727988109000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72086-4_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720857","9783031720864"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72086-4_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}