{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:43:35Z","timestamp":1758361415191,"version":"3.44.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032053244"},{"type":"electronic","value":"9783032053251"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-05325-1_27","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:27Z","timestamp":1758308727000},"page":"279-288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["InstructX2X: An Interpretable Local Editing Model for\u00a0Counterfactual Medical Image Generation"],"prefix":"10.1007","author":[{"given":"Hyungi","family":"Min","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taeseung","family":"You","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangyeul","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeongjae","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungzoon","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"27_CR1","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"27_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-1002-x","volume":"20","author":"Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V.I., Consortium, P.","year":"2020","unstructured":"Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V.I., Consortium, P.: Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med. Inform. Decis. Mak. 20, 1\u20139 (2020)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Bannur, S., Hyland, S., Liu, Q., Perez-Garcia, F., Ilse, M., Castro, D.C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et\u00a0al.: Learning to exploit temporal structure for biomedical vision-language processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 15016\u201315027 (2023)","DOI":"10.1109\/CVPR52729.2023.01442"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Bedel, H.A., \u00c7ukur, T.: Dreamr: Diffusion-driven counterfactual explanation for functional mri. IEEE Transactions on Medical Imaging (2024)","DOI":"10.1109\/TMI.2024.3507008"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Bluethgen, C., Chambon, P., Delbrouck, J.B., van\u00a0der Sluijs, R., Po\u0142acin, M., Zambrano\u00a0Chaves, J.M., Abraham, T.M., Purohit, S., Langlotz, C.P., Chaudhari, A.S.: A vision\u2013language foundation model for the generation of realistic chest x-ray images. Nature Biomedical Engineering pp. 1\u201313 (2024)","DOI":"10.1038\/s41551-024-01246-y"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Boecking, B., Usuyama, N., Bannur, S., Castro, D.C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et\u00a0al.: Making the most of text semantics to improve biomedical vision\u2013language processing. In: European conference on computer vision. pp. 1\u201321. Springer (2022)","DOI":"10.1007\/978-3-031-20059-5_1"},{"key":"27_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110786","volume":"162","author":"K Borys","year":"2023","unstructured":"Borys, K., Schmitt, Y.A., Nauta, M., Seifert, C., Kr\u00e4mer, N., Friedrich, C.M., Nensa, F.: Explainable ai in medical imaging: An overview for clinical practitioners-beyond saliency-based xai approaches. Eur. J. Radiol. 162, 110786 (2023)","journal-title":"Eur. J. Radiol."},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 18392\u201318402 (June 2023)","DOI":"10.1109\/CVPR52729.2023.01764"},{"key":"27_CR9","unstructured":"Cohen, J.P., Brooks, R., En, S., Zucker, E., Pareek, A., Lungren, M.P., Chaudhari, A.: Gifsplanation via latent shift: a simple autoencoder approach to counterfactual generation for chest x-rays. In: Medical Imaging with Deep Learning. pp. 74\u2013104. PMLR (2021)"},{"key":"27_CR10","unstructured":"Cohen, J.P., Viviano, J.D., Bertin, P., Morrison, P., Torabian, P., Guarrera, M., Lungren, M.P., Chaudhari, A., Brooks, R., Hashir, M., et\u00a0al.: Torchxrayvision: A library of chest x-ray datasets and models. In: International Conference on Medical Imaging with Deep Learning. pp. 231\u2013249. PMLR (2022)"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Fontanella, A., Mair, G., Wardlaw, J., Trucco, E., Storkey, A.: Diffusion models for counterfactual generation and anomaly detection in brain images. IEEE Transactions on Medical Imaging (2024)","DOI":"10.1109\/TMI.2024.3460391"},{"issue":"6","key":"27_CR12","doi-asserted-by":"publisher","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","volume":"4","author":"JW Gichoya","year":"2022","unstructured":"Gichoya, J.W., Banerjee, I., Bhimireddy, A.R., Burns, J.L., Celi, L.A., Chen, L.C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.C., et al.: Ai recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health 4(6), e406\u2013e414 (2022)","journal-title":"The Lancet Digital Health"},{"key":"27_CR13","unstructured":"Gu, Y., Yang, J., Usuyama, N., Li, C., Zhang, S., Lungren, M.P., Gao, J., Poon, H.: Biomedjourney: Counterfactual biomedical image generation by instruction-learning from multimodal patient journeys. arXiv preprint arXiv:2310.10765 (2023)"},{"key":"27_CR14","first-page":"5256","volume":"35","author":"T Han","year":"2022","unstructured":"Han, T., Srinivas, S., Lakkaraju, H.: Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations. Adv. Neural. Inf. Process. Syst. 35, 5256\u20135268 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Hu, X., Gu, L., An, Q., Zhang, M., Liu, L., Kobayashi, K., Harada, T., Summers, R.M., Zhu, Y.: Expert knowledge-aware image difference graph representation learning for difference-aware medical visual question answering. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 4156\u20134165 (2023)","DOI":"10.1145\/3580305.3599819"},{"issue":"1","key":"27_CR16","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1038\/s43856-022-00220-6","volume":"2","author":"H Ieki","year":"2022","unstructured":"Ieki, H., Ito, K., Saji, M., Kawakami, R., Nagatomo, Y., Takada, K., Kariyasu, T., Machida, H., Koyama, S., Yoshida, H., et al.: Deep learning-based age estimation from chest x-rays indicates cardiovascular prognosis. Communications Medicine 2(1), 159 (2022)","journal-title":"Communications Medicine"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et\u00a0al.: Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a033, pp. 590\u2013597 (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., Pollard, T.J., Berkowitz, S.J., Greenbaum, N.R., Lungren, M.P., Deng, C.y., Mark, R.G., Horng, S.: Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data 6(1), 317 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"issue":"4","key":"27_CR19","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1038\/s41591-024-02838-6","volume":"30","author":"I Ktena","year":"2024","unstructured":"Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.A., Tanno, R., Roy, A.G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al.: Generative models improve fairness of medical classifiers under distribution shifts. Nat. Med. 30(4), 1166\u20131173 (2024)","journal-title":"Nat. Med."},{"key":"27_CR20","unstructured":"Lee, S., Kim, W.J., Chang, J., Ye, J.C.: Llm-cxr: Instruction-finetuned llm for cxr image understanding and generation. In: The Twelfth International Conference on Learning Representations"},{"key":"27_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3389\/fninf.2013.00045","volume":"7","author":"BC Lowekamp","year":"2013","unstructured":"Lowekamp, B.C., Chen, D.T., Ib\u00e1\u00f1ez, L., Blezek, D.: The design of simpleitk. Front. Neuroinform. 7, 45 (2013)","journal-title":"Front. Neuroinform."},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Mirzaei, A., Aumentado-Armstrong, T., Brubaker, M.A., Kelly, J., Levinshtein, A., Derpanis, K.G., Gilitschenski, I.: Watch your steps: Local image and scene editing by text instructions. In: ECCV (2024)","DOI":"10.1007\/978-3-031-72920-1_7"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Garc\u00eda, F., Bond-Taylor, S., Sanchez, P.P., van Breugel, B., Castro, D.C., Sharma, H., Salvatelli, V., Wetscherek, M.T., Richardson, H., Lungren, M.P., et\u00a0al.: Radedit: stress-testing biomedical vision models via diffusion image editing. In: European Conference on Computer Vision. pp. 358\u2013376. Springer (2024)","DOI":"10.1007\/978-3-031-73254-6_21"},{"issue":"5","key":"27_CR24","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence 1(5), 206\u2013215 (2019)","journal-title":"Nature machine intelligence"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Sanchez, P., Kascenas, A., Liu, X., O\u2019Neil, A.Q., Tsaftaris, S.A.: What is healthy? generative counterfactual diffusion for lesion localization. In: MICCAI Workshop on Deep Generative Models. pp. 34\u201344. Springer (2022)","DOI":"10.1007\/978-3-031-18576-2_4"},{"key":"27_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102721","volume":"84","author":"S Singla","year":"2023","unstructured":"Singla, S., Eslami, M., Pollack, B., Wallace, S., Batmanghelich, K.: Explaining the black-box smoothly\u2013a counterfactual approach. Med. Image Anal. 84, 102721 (2023)","journal-title":"Med. Image Anal."},{"key":"27_CR27","unstructured":"Sun, S., Woerner, S., Maier, A., Koch, L.M., Baumgartner, C.F.: Inherently interpretable multi-label classification using class-specific counterfactuals. In: Medical Imaging with Deep Learning. pp. 937\u2013956. PMLR (2024)"},{"key":"27_CR28","unstructured":"White, A., d\u2019Avila Garcez, A.: Measurable counterfactual local explanations for any classifier. In: ECAI 2020, pp. 2529\u20132535. IOS Press (2020)"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Xia, T., Roschewitz, M., De\u00a0Sousa\u00a0Ribeiro, F., Jones, C., Glocker, B.: Mitigating attribute amplification in counterfactual image generation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 546\u2013556. Springer (2024)","DOI":"10.1007\/978-3-031-72117-5_51"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05325-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:32Z","timestamp":1758308732000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05325-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032053244","9783032053251"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05325-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests.","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":"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":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}