{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:19:36Z","timestamp":1776212376345,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031263538","type":"print"},{"value":"9783031263545","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26354-5_2","type":"book-chapter","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T17:02:56Z","timestamp":1676134976000},"page":"21-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["HarDNet-DFUS: Enhancing Backbone and\u00a0Decoder of\u00a0HarDNet-MSEG for\u00a0Diabetic Foot Ulcer Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9143-1129","authenticated-orcid":false,"given":"Ting-Yu","family":"Liao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3399-6639","authenticated-orcid":false,"given":"Ching-Hui","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4629-4940","authenticated-orcid":false,"given":"Yu-Wen","family":"Lo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5863-1735","authenticated-orcid":false,"given":"Kuan-Ying","family":"Lai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4040-0108","authenticated-orcid":false,"given":"Po-Huai","family":"Shen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4106-8082","authenticated-orcid":false,"given":"Youn-Long","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"key":"2_CR1","unstructured":"Cassidy, B., et al.: The DFUC 2020 dataset: analysis towards diabetic foot ulcer detection. touchREVIEWS in Endocrinol. 17, 5\u201311 (2021). https:\/\/doi.org\/10.17925\/EE.2021.17.1.5. https:\/\/www.touchendocrinology.com\/diabetes\/journal-articles\/the-dfuc-2020-dataset-analysis-towards-diabetic-foot-ulcer-detection\/1"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Chao, P., Kao, C.Y., Ruan, Y.S., Huang, C.H., Lin, Y.L.: HarDNet: a low memory traffic network. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3552\u20133561 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00365","DOI":"10.1109\/ICCV.2019.00365"},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.diabres.2018.02.023","volume":"138","author":"N Cho","year":"2018","unstructured":"Cho, N., et al.: IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diab. Res. Clin. Pract. 138, 271\u2013281 (2018). https:\/\/doi.org\/10.1016\/j.diabres.2018.02.023","journal-title":"Diab. Res. Clin. Pract."},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904\u20131916 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2_CR6","unstructured":"Huang, C.H., Wu, H.Y., Lin, Y.L.: HarDNet-MSEG: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 Mean Dice and 86 FPS. arXiv preprint arXiv:2101.07172 (2021)"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Jha, D., Riegler, M.A., Johansen, D., Halvorsen, P., Johansen, H.: DoubleU-Net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 558\u2013564 (2020). https:\/\/doi.org\/10.1109\/CBMS49503.2020.00111","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., et al.: ResUNet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225\u20132255 (2019). https:\/\/doi.org\/10.1109\/ISM46123.2019.00049","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Kendrick, C., et al.: Translating clinical delineation of diabetic foot ulcers into machine interpretable segmentation (2022). https:\/\/doi.org\/10.48550\/ARXIV.2204.11618","DOI":"10.48550\/ARXIV.2204.11618"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"2_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Tolstikhin, I.O., et al.: MLP-mixer: an all-MLP architecture for vision. In: Advances in Neural Information Processing Systems, vol. 34, pp. 24261\u201324272 (2021). https:\/\/doi.org\/10.48550\/ARXIV.2105.01601","DOI":"10.48550\/ARXIV.2105.01601"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1571\u20131580 (2020). https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00203","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Wei, J., Wang, S., Huang, Q.: F$$^3$$Net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019). https:\/\/doi.org\/10.48550\/ARXIV.1911.11445","DOI":"10.48550\/ARXIV.1911.11445"},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 12077\u201312090 (2021). https:\/\/doi.org\/10.48550\/ARXIV.2105.15203","DOI":"10.48550\/ARXIV.2105.15203"},{"key":"2_CR16","unstructured":"Yan, H., Zhang, C., Wu, M.: Lawin transformer: improving semantic segmentation transformer with multi-scale representations via large window attention. arXiv preprint arXiv:2201.01615 (2022)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Yap, M.H., Kendrick, C., Reeves, N.D., Goyal, M., Pappachan, J.M., Cassidy, B.: Development of diabetic foot ulcer datasets: an overview. Diab. Foot Ulcers Grand Challenge 1\u201318 (2021)","DOI":"10.1007\/978-3-030-94907-5_1"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Yap, M.H., et al.: Diabetic foot ulcers grand challenge 2022 (2021). https:\/\/doi.org\/10.5281\/zenodo.6389665","DOI":"10.5281\/zenodo.6389665"},{"key":"2_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Diabetic Foot Ulcers Grand Challenge"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26354-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T17:04:14Z","timestamp":1676135054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26354-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263538","9783031263545"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26354-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DFUC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Diabetic Foot Ulcers Grand Challenge","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dfuc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dfu-challenge.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5 challenge papers and 3 post-challenge papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}