{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T23:10:30Z","timestamp":1759705830392,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032067739"},{"type":"electronic","value":"9783032067746"}],"license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-06774-6_6","type":"book-chapter","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T07:58:17Z","timestamp":1759564697000},"page":"74-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anatomically-Focused Patches for\u00a0Lightweight and\u00a0Explainable Knee OA Grading"],"prefix":"10.1007","author":[{"given":"Tien-En","family":"Chang","sequence":"first","affiliation":[]},{"given":"Herve","family":"Lombaert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Altman, R.D., Gold, G.: Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthritis and cartilage (2007)","DOI":"10.1016\/j.joca.2006.11.009"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Antony, J., McGuinness, K., Moran, K., O\u2019Connor, N.E.: Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks. Mach. Learn. Data Min. Pattern Recogn. (2017)","DOI":"10.1007\/978-3-319-62416-7_27"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Bayramoglu, N., Nieminen, M.T., Saarakkala, S.: A lightweight CNN and joint shape-joint space (JS$$^2$$) descriptor for radiological osteoarthritis detection. Med. Image Understanding Anal. (2020)","DOI":"10.1007\/978-3-030-52791-4_26"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Bobek, S., Ba\u0142aga, P., Nalepa, G.J.: Towards model-agnostic ensemble explanations. In: International Conference on Computational Science (2021)","DOI":"10.1007\/978-3-030-77970-2_4"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (2018)","DOI":"10.1109\/WACV.2018.00097"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Chen, P., Gao, L., Shi, X., Allen, K., Lin, Y.: Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Comput. Med. Imaging Graph. (2019)","DOI":"10.1016\/j.compmedimag.2019.06.002"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Cohen, J.: Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol. Bull. (1968)","DOI":"10.1037\/h0026256"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Cui, A., Li, H., Wang, D., Zhong, J., Chen, Y., Lu, H.: Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies. EClinicalMedicine (2020)","DOI":"10.1016\/j.eclinm.2020.100587"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Culvenor, A.G., Engen, C.N., \u00d8iestad, B.E., Engebretsen, L., Risberg, M.A.: Defining the presence of radiographic knee osteoarthritis: a comparison between the Kellgren and Lawrence system and OARSI atlas criteria. Knee Surgery, Sports Traumatology, Arthroscopy (2015)","DOI":"10.1016\/j.joca.2014.02.500"},{"key":"6_CR10","unstructured":"Gildenblat, J., contributors: Pytorch library for CAM methods. https:\/\/github.com\/jacobgil\/pytorch-grad-cam (2025\/06\/19)"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR12","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning (2018)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: LayerCAM: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. (2021)","DOI":"10.1109\/TIP.2021.3089943"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Kellgren, J., Lawrence, J.: Radiological assessment of osteo-arthrosis. Ann. Rheumatic Diseases (1957)","DOI":"10.1136\/ard.16.4.494"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Lindner, C., Bromiley, P.A., Ionita, M.C., Cootes, T.F.: Robust and accurate shape model matching using random forest regression-voting. IEEE Trans. Pattern Anal. Mach. Intell. (2014)","DOI":"10.1109\/TPAMI.2014.2382106"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Lindner, C., Thiagarajah, S., Wilkinson, J.M., Wallis, G.A., Cootes, T.F., arcOGEN Consortium, et\u00a0al.: Fully automatic segmentation of the proximal femur using random forest regression voting. IEEE Trans. Med. Imaging (2013)","DOI":"10.1109\/TMI.2013.2258030"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Patron, A., Annala, L., Lainiala, O., Paloneva, J., \u00c4yr\u00e4m\u00f6, S.: An automatic method for assessing spiking of tibial tubercles associated with knee osteoarthritis. Diagnostics (2022)","DOI":"10.2139\/ssrn.4155105"},{"key":"6_CR18","unstructured":"Ramaswamy, H.G., et\u00a0al.: Ablation-CAM: visual explanations for deep convolutional network via gradient-free localization. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (2020)"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Tiulpin, A., Saarakkala, S.: Automatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks. Diagnostics (2020)","DOI":"10.1016\/j.joca.2020.02.480"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S.: Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci. Rep. (2018)","DOI":"10.1038\/s41598-018-20132-7"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Tiulpin, A., Thevenot, J., Rahtu, E., Saarakkala, S.: A novel method for automatic localization of joint area on knee plain radiographs. Image Anal. (2017)","DOI":"10.1007\/978-3-319-59129-2_25"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Yang, X., Tang, P., Zou, K., Dai, D.: HCGN: hierarchical convolution and graph network for predicting knee osteoarthritis. In: 2024 IEEE International Conference on Medical Artificial Intelligence (MedAI) (2024)","DOI":"10.1109\/MedAI62885.2024.00018"}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06774-6_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T22:41:37Z","timestamp":1759704097000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06774-6_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,5]]},"ISBN":["9783032067739","9783032067746"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06774-6_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,5]]},"assertion":[{"value":"5 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Democratic People's Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}