{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T19:06:57Z","timestamp":1779304017669,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819571406","type":"print"},{"value":"9789819571413","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-7141-3_28","type":"book-chapter","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T18:24:18Z","timestamp":1779301458000},"page":"424-439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble Transformer-Based Multiple Instance Learning for\u00a0Predicting Neoadjuvant Chemotherapy Response from\u00a0Breast Cancer Biopsy Whole-Slide Images"],"prefix":"10.1007","author":[{"given":"Kaixin","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaining","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenshui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqiang","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongliang","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuehong","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"28_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209\u2013249 (2021)","journal-title":"CA Cancer J. Clin."},{"key":"28_CR2","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1200\/JCO.20.03399","volume":"39","author":"LA Korde","year":"2021","unstructured":"Korde, L.A., et al.: Neoadjuvant chemotherapy, endocrine therapy, and targeted therapy for breast cancer: ASCO guideline. J. Clin. Oncol. 39, 1485\u20131505 (2021)","journal-title":"J. Clin. Oncol."},{"key":"28_CR3","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","volume":"384","author":"P Cortazar","year":"2014","unstructured":"Cortazar, P., et al.: Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384, 164\u2013172 (2014)","journal-title":"Lancet"},{"key":"28_CR4","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/S1470-2045(21)00589-1","volume":"23","author":"C Yau","year":"2022","unstructured":"Yau, C., et al.: Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients. Lancet Oncol 23, 149\u2013160 (2022)","journal-title":"Lancet Oncol"},{"key":"28_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40164-022-00363-1","volume":"12","author":"L Guo","year":"2023","unstructured":"Guo, L., et al.: Breast cancer heterogeneity and its implication in personalized precision therapy. Exp. Hematol. Oncol. 12, 3 (2023)","journal-title":"Exp. Hematol. Oncol."},{"key":"28_CR6","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1002\/cac2.12012","volume":"40","author":"Y Jiang","year":"2020","unstructured":"Jiang, Y., Yang, M., Wang, S., Li, X., Sun, Y.: Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun. 40, 154\u2013166 (2020)","journal-title":"Cancer Commun."},{"key":"28_CR7","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","volume":"33","author":"A Madabhushi","year":"2016","unstructured":"Madabhushi, A., Lee, G.: Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33, 170\u2013175 (2016)","journal-title":"Med. Image Anal."},{"key":"28_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.998222","volume":"12","author":"J Liao","year":"2023","unstructured":"Liao, J., et al.: Artificial intelligence assists precision medicine in cancer treatment. Front. Oncol. 12, 998222 (2023)","journal-title":"Front. Oncol."},{"key":"28_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559","volume":"81","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022)","journal-title":"Med. Image Anal."},{"key":"28_CR10","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1186\/s12885-023-10817-2","volume":"23","author":"J Zhang","year":"2023","unstructured":"Zhang, J., et al.: Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients. BMC Cancer 23, 431 (2023)","journal-title":"BMC Cancer"},{"key":"28_CR11","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1186\/s13058-023-01726-0","volume":"25","author":"W Aswolinskiy","year":"2023","unstructured":"Aswolinskiy, W., et al.: PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning. Breast Cancer Res. 25, 142 (2023)","journal-title":"Breast Cancer Res."},{"key":"28_CR12","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.breast.2022.10.004","volume":"66","author":"B Li","year":"2022","unstructured":"Li, B., et al.: Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Breast 66, 183\u2013190 (2022)","journal-title":"Breast"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Mao, N., et al.: A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer. Sci. Adv. 11, eadr1576 (2025)","DOI":"10.1126\/sciadv.adr1576"},{"key":"28_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2025.108803","volume":"267","author":"Q Zhou","year":"2025","unstructured":"Zhou, Q., et al.: Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer. Comput. Methods Programs Biomed. 267, 108803 (2025)","journal-title":"Comput. Methods Programs Biomed."},{"key":"28_CR15","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1016\/S1470-2045(22)00613-1","volume":"23","author":"HM Kuerer","year":"2022","unstructured":"Kuerer, H.M., et al.: Eliminating breast surgery for invasive breast cancer in exceptional responders to neoadjuvant systemic therapy: a multicentre, single-arm, phase 2 trial. Lancet Oncol 23, 1517\u20131524 (2022)","journal-title":"Lancet Oncol"},{"key":"28_CR16","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.33933","volume":"6","author":"HM Johnson","year":"2023","unstructured":"Johnson, H.M., et al.: Patient-reported outcomes of omission of breast surgery following neoadjuvant systemic therapy: a nonrandomized clinical trial. JAMA Netw. Open 6, e2333933 (2023)","journal-title":"JAMA Netw. Open"},{"key":"28_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.21037\/tbcr-24-65","volume":"6","author":"F Phang","year":"2025","unstructured":"Phang, F., Weiss, A.: Omission of breast surgery in exceptional responders after neoadjuvant chemotherapy-what are future possibilities?-a narrative review. Transl. Breast Cancer Res. 6, 13 (2025)","journal-title":"Transl. Breast Cancer Res."},{"key":"28_CR18","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.clbc.2024.01.021","volume":"24","author":"M-K Tasoulis","year":"2024","unstructured":"Tasoulis, M.-K., Lee, H.-B., Kuerer, H.M.: Omission of breast surgery in exceptional responders. Clin. Breast Cancer 24, 310\u2013318 (2024)","journal-title":"Clin. Breast Cancer"},{"key":"28_CR19","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1001\/jamaoncol.2025.0207","volume":"11","author":"HM Kuerer","year":"2025","unstructured":"Kuerer, H.M., et al.: Selective elimination of breast surgery for invasive breast cancer: a nonrandomized clinical trial. JAMA Oncol. 11, 529\u2013534 (2025)","journal-title":"JAMA Oncol."},{"key":"28_CR20","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10549-017-4164-1","volume":"163","author":"LH Zetterlund","year":"2017","unstructured":"Zetterlund, L.H., et al.: Swedish prospective multicenter trial evaluating sentinel lymph node biopsy after neoadjuvant systemic therapy in clinically node-positive breast cancer. Breast Cancer Res. Treat. 163, 103\u2013110 (2017)","journal-title":"Breast Cancer Res. Treat."},{"key":"28_CR21","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1097\/SLA.0000000000002313","volume":"267","author":"HM Kuerer","year":"2018","unstructured":"Kuerer, H.M., et al.: A clinical feasibility trial for identification of exceptional responders in whom breast cancer surgery can be eliminated following neoadjuvant systemic therapy. Ann. Surg. 267, 946\u2013951 (2018)","journal-title":"Ann. Surg."},{"key":"28_CR22","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1001\/jamaoncol.2016.1897","volume":"2","author":"LM Spring","year":"2016","unstructured":"Spring, L.M., et al.: Neoadjuvant endocrine therapy for estrogen receptor-positive breast cancer: a systematic review and meta-analysis. JAMA Oncol. 2, 1477 (2016)","journal-title":"JAMA Oncol."},{"key":"28_CR23","doi-asserted-by":"publisher","first-page":"e327","DOI":"10.1016\/S1470-2045(20)30741-5","volume":"22","author":"L Biganzoli","year":"2021","unstructured":"Biganzoli, L., et al.: Updated recommendations regarding the management of older patients with breast cancer: a joint paper from the European society of breast cancer specialists (EUSOMA) and the international society of geriatric oncology (SIOG). Lancet Oncol. 22, e327\u2013e340 (2021)","journal-title":"Lancet Oncol."},{"key":"28_CR24","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1056\/NEJMoa1804710","volume":"379","author":"JA Sparano","year":"2018","unstructured":"Sparano, J.A., et al.: Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N. Engl. J. Med. 379, 111\u2013121 (2018)","journal-title":"N. Engl. J. Med."},{"key":"28_CR25","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42, 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Behavioural and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7141-3_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T18:24:19Z","timestamp":1779301459000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7141-3_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819571406","9789819571413"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7141-3_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"No personally identifiable information is included in the images or data published in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent-to-Publish Statement"}},{"value":"BESC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Behavioural and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong SAR","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"besc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/besc-conf.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}