{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T04:19:22Z","timestamp":1779164362541,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736963","type":"proceedings-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:30:13Z","timestamp":1754055013000},"page":"404-414","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["FlanS: A Foundation Model for Free-Form Language-based Segmentation in Medical Images"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8631-9634","authenticated-orcid":false,"given":"Longchao","family":"Da","sequence":"first","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6620-8505","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Amazon Web Serives, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-8963","authenticated-orcid":false,"given":"Xiaojian","family":"Xu","sequence":"additional","affiliation":[{"name":"GE-HealthCare, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0038-5081","authenticated-orcid":false,"given":"Parminder","family":"Bhatia","sequence":"additional","affiliation":[{"name":"GE-HealthCare, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0123-5157","authenticated-orcid":false,"given":"Taha","family":"Kass-Hout","sequence":"additional","affiliation":[{"name":"GE-HealthCare, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3735-1635","authenticated-orcid":false,"given":"Hua","family":"Wei","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3869-6942","authenticated-orcid":false,"given":"Cao","family":"Xiao","sequence":"additional","affiliation":[{"name":"GE-HealthCare, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Michela Antonelli Annika Reinke Spyridon Bakas Keyvan Farahani Annette Kopp-Schneider Bennett A Landman Geert Litjens Bjoern Menze Olaf Ronneberger Ronald M Summers et al. 2022. The medical segmentation decathlon. Nature communications Vol. 13 1 (2022) 4128.","DOI":"10.1038\/s41467-022-30695-9"},{"key":"e_1_3_2_2_2_1","volume-title":"Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, and Dorit Merhof.","author":"Azad Reza","year":"2024","unstructured":"Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, and Dorit Merhof. 2024. Medical image segmentation review: The success of u-net. IEEE Transactions on Pattern Analysis and Machine Intelligence(2024)."},{"key":"e_1_3_2_2_3_1","volume-title":"Borjan Gagoski, P Ellen Grant, and Polina Golland.","author":"Billot Benjamin","year":"2024","unstructured":"Benjamin Billot, Neel Dey, Daniel Moyer, Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski, P Ellen Grant, and Polina Golland. 2024. SE (3)-equivariant and noise-invariant 3D rigid motion tracking in brain MRI. IEEE Transactions on Medical Imaging(2024)."},{"key":"e_1_3_2_2_4_1","unstructured":"Michael M Bronstein Joan Bruna Taco Cohen and Petar Velickovic. 2021. Geometric deep learning: Grids groups graphs geodesics and gauges. arXiv preprint arXiv:2104.13478(2021)."},{"key":"e_1_3_2_2_5_1","volume-title":"UniverSeg: Universal Medical Image Segmentation. International Conference on Computer Vision(2023)","author":"Victor Ion","unstructured":"Victor Ion Butoi*, Jose Javier Gonzalez Ortiz*, Tianyu Ma, Mert R. Sabuncu, John Guttag, and Adrian V. Dalca. 2023. UniverSeg: Universal Medical Image Segmentation. International Conference on Computer Vision(2023)."},{"key":"e_1_3_2_2_6_1","volume-title":"European conference on computer vision. Springer, 205-218","author":"Cao Hu","year":"2022","unstructured":"Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. 2022. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision. Springer, 205-218."},{"key":"e_1_3_2_2_7_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=WE4qe9xlnQw","author":"Cesa Gabriele","year":"2022","unstructured":"Gabriele Cesa, Leon Lang, and Maurice Weiler. 2022. A Program to Build E(N)-Equivariant Steerable CNNs. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=WE4qe9xlnQw"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00434"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2835303"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_2_2_11_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML).","author":"Cohen T.S.","unstructured":"T.S. Cohen and M. Welling. 2016. Group Equivariant Convolutional Networks. In Proceedings of the International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_12_1","first-page":"9142","article-title":"A general theory of equivariant cnns on homogeneous spaces","author":"Cohen Taco S","year":"2019","unstructured":"Taco S Cohen, Mario Geiger, and Maurice Weiler. 2019. A general theory of equivariant cnns on homogeneous spaces. In Advances in Neural Information Processing Systems. 9142-9153.","journal-title":"Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47425-5_33"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01601"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508"},{"key":"e_1_3_2_2_16_1","first-page":"301","article-title":"E (3) x SO (3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI","author":"Elaldi Axel","year":"2024","unstructured":"Axel Elaldi, Guido Gerig, and Neel Dey. 2024. E (3) x SO (3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI. In Medical Imaging with Deep Learning. PMLR, 301-319.","journal-title":"Medical Imaging with Deep Learning. PMLR"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01525"},{"key":"e_1_3_2_2_18_1","volume-title":"International conference on machine learning. PMLR, 3318-3328","author":"Finzi Marc","year":"2021","unstructured":"Marc Finzi, Max Welling, and Andrew Gordon Wilson. 2021. A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups. In International conference on machine learning. PMLR, 3318-3328."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.1169361"},{"key":"e_1_3_2_2_20_1","volume-title":"CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation","author":"Gu Ran","year":"2020","unstructured":"Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, S\u00e9bastien Ourselin, Tom Vercauteren, and Shaoting Zhang. 2020. CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE transactions on medical imaging, Vol. 40, 2 (2020), 699-711."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_5"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_7"},{"key":"e_1_3_2_2_23_1","unstructured":"Xinrong Hu Xiaowei Xu and Yiyu Shi. 2023. How to efficiently adapt large segmentation model (sam) to medical images. arXiv preprint arXiv:2306.13731(2023)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Fabian Isensee Jens Petersen Andre Klein David Zimmerer Paul F Jaeger Simon Kohl Jakob Wasserthal Gregor Koehler Tobias Norajitra Sebastian Wirkert et al. 2018. nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486(2018).","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"e_1_3_2_2_25_1","volume-title":"Equivariance With Learned Canonicalization Functions. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations. https:\/\/openreview.net\/forum?id=pVD1k8ge25a","author":"Kaba S\u00e9kou-Oumar","year":"2022","unstructured":"S\u00e9kou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, and Siamak Ravanbakhsh. 2022. Equivariance With Learned Canonicalization Functions. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations. https:\/\/openreview.net\/forum?id=pVD1k8ge25a"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","unstructured":"Ali Emre Kavur M. Alper Selver Oguz Dicle and N. Sinem Gezer. 2019. CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data https:\/\/doi.org\/10.5281\/zenodo.3362844","DOI":"10.5281\/zenodo.3362844"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01761"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43898-1_25"},{"key":"e_1_3_2_2_30_1","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis Patrick","year":"2020","unstructured":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fcttler, Mike Lewis, Wen-tau Yih, Tim Rockt\u00e4schel, et al., 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9459-9474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00602"},{"key":"e_1_3_2_2_32_1","unstructured":"Shengze Li Jianjian Cao Peng Ye Yuhan Ding Chongjun Tu and Tao Chen. 2024. ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation. arXiv preprint arXiv:2401.12665(2024)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02259"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01934"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/su13031224"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00695"},{"key":"e_1_3_2_2_37_1","unstructured":"Xiangde Luo Zihan Li Shaoting Zhang Wenjun Liao and Guotai Wang. 2024. Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases. (2024)."},{"key":"e_1_3_2_2_38_1","volume-title":"WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image. arXiv preprint arXiv:2111.02403(2021).","author":"Luo Xiangde","year":"2021","unstructured":"Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N Metaxas, Guotai Wang, and Shaoting Zhang. 2021. WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image. arXiv preprint arXiv:2111.02403(2021)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z"},{"key":"e_1_3_2_2_40_1","volume-title":"Nat\u00e1lia Alves, Bram de Wilde, Gregor Koehler, Yajun Wu, Manuel Wiesenfarth, Qiongjie Zhu, Guoqiang Dong, Jian He, the","author":"Ma Jun","year":"2023","unstructured":"Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Ma, Adamo Young, Cheng Zhu, Kangkang Meng, Xin Yang, Ziyan Huang, Fan Zhang, Wentao Liu, YuanKe Pan, Shoujin Huang, Jiacheng Wang, Mingze Sun, Weixin Xu, Dengqiang Jia, Jae Won Choi, Nat\u00e1lia Alves, Bram de Wilde, Gregor Koehler, Yajun Wu, Manuel Wiesenfarth, Qiongjie Zhu, Guoqiang Dong, Jian He, the FLARE Challenge Consortium, and Bo Wang. 2023. Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge. arXiv preprint arXiv:2308.05862(2023)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3100536"},{"key":"e_1_3_2_2_42_1","first-page":"50293","article-title":"Equivariant adaptation of large pretrained models","volume":"36","author":"Mondal Arnab Kumar","year":"2023","unstructured":"Arnab Kumar Mondal, Siba Smarak Panigrahi, Oumar Kaba, Sai Rajeswar Mudumba, and Siamak Ravanbakhsh. 2023. Equivariant adaptation of large pretrained models. Advances in Neural Information Processing Systems, Vol. 36 (2023), 50293-50309.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_43_1","volume-title":"Current methods in medical image segmentation. Annual review of biomedical engineering","author":"Pham Dzung L","year":"2000","unstructured":"Dzung L Pham, Chenyang Xu, and Jerry L Prince. 2000. Current methods in medical image segmentation. Annual review of biomedical engineering, Vol. 2, 1 (2000), 315-337."},{"key":"e_1_3_2_2_44_1","unstructured":"Omri Puny Matan Atzmon Heli Ben-Hamu Ishan Misra Aditya Grover Edward J Smith and Yaron Lipman. 2021. Frame averaging for invariant and equivariant network design. arXiv preprint arXiv:2110.03336(2021)."},{"key":"e_1_3_2_2_45_1","volume-title":"International conference on machine learning. PMLR, 8748-8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748-8763."},{"key":"e_1_3_2_2_46_1","volume-title":"Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine","author":"Rasmy Laila","year":"2021","unstructured":"Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi. 2021. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine, Vol. 4, 1 (2021), 86."},{"key":"e_1_3_2_2_47_1","volume-title":"Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Doll\u00e1r, and Christoph Feichtenhofer.","author":"Ravi Nikhila","year":"2024","unstructured":"Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R\u00e4dle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Doll\u00e1r, and Christoph Feichtenhofer. 2024. SAM 2: Segment Anything in Images and Videos. arXiv preprint arXiv:2408.00714(2024). https:\/\/arxiv.org\/abs\/2408.00714"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102802"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101979"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_3"},{"key":"e_1_3_2_2_51_1","unstructured":"Nathaniel Thomas Tess Smidt Steven Kearnes Lusann Yang Li Li Kai Kohlhoff and Patrick Riley. 2018. Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds. arXiv preprint arXiv:1802.08219(2018)."},{"key":"e_1_3_2_2_52_1","volume-title":"Neel Sortur, Lawson LS Wong, Robin Walters, and Robert Platt.","author":"Wang Dian","year":"2022","unstructured":"Dian Wang, Jung Yeon Park, Neel Sortur, Lawson LS Wong, Robin Walters, and Robert Platt. 2022c. The surprising effectiveness of equivariant models in domains with latent symmetry. arXiv preprint arXiv:2211.09231(2022)."},{"key":"e_1_3_2_2_53_1","volume-title":"Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, and Hadi Pouransari.","author":"Wang Haoxiang","year":"2024","unstructured":"Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, and Hadi Pouransari. 2024. SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding. https:\/\/openreview.net\/forum?id=GKau1ekOtH"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12419"},{"key":"e_1_3_2_2_55_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=wta_8Hx2KD","author":"Wang Rui","year":"2021","unstructured":"Rui Wang, Robin Walters, and Rose Yu. 2021. Incorporating Symmetry into Deep Dynamics Models for Improved Generalization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=wta_8Hx2KD"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01139"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","unstructured":"Zifeng Wang Zhenbang Wu Dinesh Agarwal and Jimeng Sun. 2022 d. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163(2022).","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"e_1_3_2_2_58_1","first-page":"14334","article-title":"General E(2)-Equivariant Steerable CNNs","author":"Weiler Maurice","year":"2019","unstructured":"Maurice Weiler and Gabriele Cesa. 2019. General E(2)-Equivariant Steerable CNNs. In Advances in Neural Information Processing Systems (NeurIPS). 14334-14345.","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_59_1","unstructured":"Marysia Winkels and Taco S Cohen. 2018. 3D G-CNNs for pulmonary nodule detection. arXiv preprint arXiv:1804.04656(2018)."},{"key":"e_1_3_2_2_60_1","unstructured":"Junde Wu Wei Ji Yuanpei Liu Huazhu Fu Min Xu Yanwu Xu and Yueming Jin. 2023. Medical sam adapter: Adapting segment anything model for medical image segmentation. arXiv preprint arXiv:2304.12620(2023)."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-82184-5"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6946"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01762"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01075"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3161829"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"crossref","unstructured":"Kaidong Zhang and Dong Liu. 2023. Customized segment anything model for medical image segmentation. arXiv preprint arXiv:2304.13785(2023).","DOI":"10.2139\/ssrn.4495221"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"e_1_3_2_2_68_1","volume-title":"Tristan Naumann, Jianfeng Gao, Angela Crabtree, Jacob Abel, Christine Moung-Wen, et al.","author":"Zhao Theodore","year":"2024","unstructured":"Theodore Zhao, Yu Gu, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Tristan Naumann, Jianfeng Gao, Angela Crabtree, Jacob Abel, Christine Moung-Wen, et al., 2024. BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once. arXiv preprint arXiv:2405.12971(2024)."},{"key":"e_1_3_2_2_69_1","first-page":"28611","article-title":"Text promptable surgical instrument segmentation with vision-language models","volume":"36","author":"Zhou Zijian","year":"2023","unstructured":"Zijian Zhou, Oluwatosin Alabi, Meng Wei, Tom Vercauteren, and Miaojing Shi. 2023. Text promptable surgical instrument segmentation with vision-language models. Advances in Neural Information Processing Systems, Vol. 36 (2023), 28611-28623.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_70_1","unstructured":"Jiayuan Zhu Yunli Qi and Junde Wu. 2024. Medical sam 2: Segment medical images as video via segment anything model 2. arXiv preprint arXiv:2408.00874(2024)."},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2022.XVIII.071"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736963","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:14:53Z","timestamp":1777572893000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":71,"alternative-id":["10.1145\/3711896.3736963","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736963","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}