{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:03:55Z","timestamp":1758845035808,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032055583","type":"print"},{"value":"9783032055590","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-05559-0_15","type":"book-chapter","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:33:09Z","timestamp":1758767589000},"page":"145-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Synergies Between Convolutional Neural Networks and\u00a0Transformers for\u00a0Breast Cancer Segmentation"],"prefix":"10.1007","author":[{"given":"Carlos","family":"Santiago","sequence":"first","affiliation":[]},{"given":"Jacinto C.","family":"Nascimento","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In: International MICCAI Brainlesion Workshop, pp. 272\u2013284. Springer (2021)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Anderson, B.O., et al.: The global breast cancer initiative: a strategic collaboration to strengthen health care for non-communicable diseases. Lancet Oncol. 22(5), 578\u2013581 (2021)","DOI":"10.1016\/S1470-2045(21)00071-1"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Calisto, F.M., et al.: BreastScreening-AI: evaluating medical intelligent agents for human-AI interactions. Artif. Intell. Med. 127, 102285 (2022)","DOI":"10.1016\/j.artmed.2022.102285"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Moreira, I.C., et al.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236\u2013248 (2012)","DOI":"10.1016\/j.acra.2011.09.014"},{"key":"15_CR5","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. CoRR abs\/2102.04306 (2021), https:\/\/arxiv.org\/abs\/2102.04306"},{"key":"15_CR6","unstructured":"Howard, A., et al.: Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation. CoRR abs\/1801.04381 (2018), http:\/\/arxiv.org\/abs\/1801.04381"},{"key":"15_CR7","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Diogo, P., et al.: Weakly-supervised diagnosis and detection of breast cancer using deep multiple instance learning. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20134. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230448"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Lee, R.S., et al.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4(1), \u00a01\u20139 (2017)","DOI":"10.1038\/sdata.2017.177"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Wang, T.,\u00a0et al., : O-net: a novel framework with deep fusion of cnn and transformer for simultaneous segmentation and classification. Front. Neurosci. 16 (2022). https:\/\/doi.org\/10.3389\/fnins.2022.876065, https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2022.876065","DOI":"10.3389\/fnins.2022.876065"},{"key":"15_CR12","unstructured":"et\u00a0al., V.R.: Tackling bias in the dice similarity coefficient: introducing nDSC for white matter lesion segmentation. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), IEEE (2023)"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Li, Z.,\u00a0et al.: TFCNs: a CNN-transformer hybrid network for medical image segmentation. In: Artificial Neural Networks and Machine Learning \u2013 ICANN 2022, pp. 781\u2013792. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-15937-4_65"},{"key":"15_CR15","unstructured":"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)"},{"key":"15_CR16","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"15_CR18","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Khan, S., Islam, N., Jan, Z., Din, I.U., Rodrigues, J.J.C.: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn. Lett. 125, 1\u20136 (2019)","DOI":"10.1016\/j.patrec.2019.03.022"},{"key":"15_CR20","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"15_CR22","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"15_CR24","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"15_CR25","doi-asserted-by":"publisher","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 36\u201346. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_4","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"15_CR26","doi-asserted-by":"publisher","unstructured":"Xie, Y., Zhang, J., Shen, C., Xia, Y.: CoTr: efficiently bridging CNN and transformer for 3d medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 171\u2013180. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_16","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"15_CR27","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05559-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:33:19Z","timestamp":1758767599000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05559-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032055583","9783032055590"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05559-0_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","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":"Deep-Breath","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care","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 (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":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"deep-breath2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/deep-breath-miccai.github.io\/deepbreath-2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}