{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:04:02Z","timestamp":1770750242505,"version":"3.50.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,26]]},"DOI":"10.1145\/3706599.3719719","type":"proceedings-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T20:44:19Z","timestamp":1745441059000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Interactively Assisting Glaucoma Diagnosis with an Expert Knowledge-Distilled Vision Transformer"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5187-200X","authenticated-orcid":false,"given":"Ziheng","family":"Li","sequence":"first","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1856-5627","authenticated-orcid":false,"given":"Haowen","family":"Wei","sequence":"additional","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6200-8900","authenticated-orcid":false,"given":"Kuang","family":"Sun","sequence":"additional","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0038-3083","authenticated-orcid":false,"given":"Leyi","family":"Cui","sequence":"additional","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1898-6834","authenticated-orcid":false,"given":"David","family":"Li","sequence":"additional","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9978-7090","authenticated-orcid":false,"given":"Steven","family":"Feiner","sequence":"additional","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5589-8151","authenticated-orcid":false,"given":"Kaveri","family":"Thakoor","sequence":"additional","affiliation":[{"name":"Columbia University, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Samira Abnar and Willem Zuidema. 2020. Quantifying attention flow in transformers. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2005.00928 (2020).","DOI":"10.18653\/v1\/2020.acl-main.385"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Adekanmi\u00a0Adeyinka Adegun Serestina Viriri and Roseline\u00a0Oluwaseun Ogundokun. 2021. Deep learning approach for medical image analysis. Computational Intelligence and Neuroscience 2021 (2021) 1\u20139.","DOI":"10.1155\/2021\/6215281"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376718"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19803-8_40"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Francisco\u00a0Maria Calisto Carlos Santiago Nuno Nunes and Jacinto\u00a0C Nascimento. 2021. Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification. International Journal of Human-Computer Studies 150 (2021) 102607.","DOI":"10.1016\/j.ijhcs.2021.102607"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00045"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00084"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Xuxin Chen Ximin Wang Ke Zhang Kar-Ming Fung Theresa\u00a0C Thai Kathleen Moore Robert\u00a0S Mannel Hong Liu Bin Zheng and Yuchen Qiu. 2022. Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis 79 (2022) 102444.","DOI":"10.1016\/j.media.2022.102444"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Xiaoming Chen Ying Xue Xiaoyan Wu Yi Zhong Huiying Rao Heng Luo and Zuquan Weng. 2023. Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images. Translational Vision Science & Technology 12 1 (2023) 29\u201329.","DOI":"10.1167\/tvst.12.1.29"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Mohamed Elsharkawy Ahmed Sharafeldeen Ahmed Soliman Fahmi Khalifa Mohammed Ghazal Eman El-Daydamony Ahmed Atwan Harpal\u00a0Singh Sandhu and Ayman El-Baz. 2022. A novel computer-aided diagnostic system for early detection of diabetic retinopathy using 3D-OCT higher-order spatial appearance model. Diagnostics 12 2 (2022) 461.","DOI":"10.3390\/diagnostics12020461"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Ahmed ElTanboly Marwa Ismail Ahmed Shalaby Andy Switala Ayman El-Baz Shlomit Schaal Georgy Gimel\u2019farb and Magdi El-Azab. 2017. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Medical physics 44 3 (2017) 914\u2013923.","DOI":"10.1002\/mp.12071"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Bradley\u00a0J Erickson Panagiotis Korfiatis Zeynettin Akkus and Timothy\u00a0L Kline. 2017. Machine learning for medical imaging. Radiographics 37 2 (2017) 505\u2013515.","DOI":"10.1148\/rg.2017160130"},{"key":"e_1_3_3_2_14_2","unstructured":"Hugging Face. 2023. timm vit_small_patch32_224.augreg_in21k_ft_in1k. https:\/\/huggingface.co\/timm\/vit_small_patch32_224.augreg_in21k_ft_in1k. Accessed: 2024-08-28."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Leyuan Fang Chong Wang Shutao Li Hossein Rabbani Xiangdong Chen and Zhimin Liu. 2019. Attention to lesion: Lesion-aware convolutional neural network for retinal optical coherence tomography image classification. IEEE transactions on medical imaging 38 8 (2019) 1959\u20131970.","DOI":"10.1109\/TMI.2019.2898414"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_6"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Yuki Hagiwara Joel En\u00a0Wei Koh Jen\u00a0Hong Tan Sulatha\u00a0V Bhandary Augustinus Laude Edward\u00a0J Ciaccio Louis Tong and U\u00a0Rajendra Acharya. 2018. Computer-aided diagnosis of glaucoma using fundus images: A review. Computer methods and programs in biomedicine 165 (2018) 1\u201312.","DOI":"10.1016\/j.cmpb.2018.07.012"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Bilal Hassan Shiyin Qin Ramsha Ahmed Taimur Hassan Abdel\u00a0Hakeem Taguri Shahrukh Hashmi and Naoufel Werghi. 2021. Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy. Computers in Biology and Medicine 136 (2021) 104727.","DOI":"10.1016\/j.compbiomed.2021.104727"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Md\u00a0Shakhawat Hossain Galib\u00a0Muhammad Shahriar MM\u00a0Mahbubul Syeed Mohammad\u00a0Faisal Uddin Mahady Hasan Shingla Shivam and Suresh Advani. 2023. Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images. Scientific Reports 13 1 (2023) 11314.","DOI":"10.1038\/s41598-023-38109-6"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"David Huang Eric\u00a0A Swanson Charles\u00a0P Lin Joel\u00a0S Schuman William\u00a0G Stinson Warren Chang Michael\u00a0R Hee Thomas Flotte Kenton Gregory Carmen\u00a0A Puliafito et\u00a0al. 1991. Optical coherence tomography. science 254 5035 (1991) 1178\u20131181.","DOI":"10.1126\/science.1957169"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Bulat Ibragimov and Claudia Mello-Thoms. 2024. The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review. IEEE journal of biomedical and health informatics (2024).","DOI":"10.1109\/JBHI.2024.3371893"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Huiyan Jiang Zhaoshuo Diao Tianyu Shi Yang Zhou Feiyu Wang Wenrui Hu Xiaolin Zhu Shijie Luo Guoyu Tong and Yu-Dong Yao. 2023. A review of deep learning-based multiple-lesion recognition from medical images: classification detection and segmentation. Computers in Biology and Medicine (2023) 106726.","DOI":"10.1016\/j.compbiomed.2023.106726"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC40787.2023.10340746"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Christopher\u00a0J Kelly Alan Karthikesalingam Mustafa Suleyman Greg Corrado and Dominic King. 2019. Key challenges for delivering clinical impact with artificial intelligence. BMC medicine 17 (2019) 1\u20139.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Mijung Kim Jong\u00a0Chul Han Seung\u00a0Hyup Hyun Olivier Janssens Sofie Van\u00a0Hoecke Changwon Kee and Wesley De\u00a0Neve. 2019. Medinoid: computer-aided diagnosis and localization of glaucoma using deep learning. Applied Sciences 9 15 (2019) 3064.","DOI":"10.3390\/app9153064"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"SV\u00a0Mahesh Kumar and R Gunasundari. 2023. Computational intelligence in eye disease diagnosis: a comparative study. Medical & Biological Engineering & Computing 61 3 (2023) 593\u2013615.","DOI":"10.1007\/s11517-022-02737-3"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Yutao Ma Tao Xu Xiaolei Huang Xiaofang Wang Canyu Li Jason Jerwick Yuan Ning Xianxu Zeng Baojin Wang Yihong Wang et\u00a0al. 2019. Computer-aided diagnosis of label-free 3-D optical coherence microscopy images of human cervical tissue. IEEE Transactions on Biomedical Engineering 66 9 (2019) 2447\u20132456.","DOI":"10.1109\/TBME.2018.2890167"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43904-9_66"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Delaram Mirzania Atalie\u00a0C Thompson and Kelly\u00a0W Muir. 2021. Applications of deep learning in detection of glaucoma: a systematic review. European Journal of Ophthalmology 31 4 (2021) 1618\u20131642.","DOI":"10.1177\/1120672120977346"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Raffaele Nuzzi Giacomo Boscia Paola Marolo and Federico Ricardi. 2021. The impact of artificial intelligence and deep learning in eye diseases: a review. Frontiers in Medicine 8 (2021) 710329.","DOI":"10.3389\/fmed.2021.710329"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_43"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Drew Prinster Amama Mahmood Suchi Saria Jean Jeudy Cheng\u00a0Ting Lin Paul\u00a0H Yi and Chien-Ming Huang. 2024. Care to explain? AI explanation types differentially impact chest radiograph diagnostic performance and physician trust in AI. Radiology 313 2 (2024) e233261.","DOI":"10.1148\/radiol.233261"},{"key":"e_1_3_3_2_33_2","unstructured":"Joel Salas Ryan Zukerman Omar Moussa Sophie\u00a0Z Gu Ari Leshno Jeffrey\u00a0M Liebmann George Cioffi and Kaveri Thakoor. 2023. Impact of AI on Retrospective Glaucoma Diagnosis. Investigative Ophthalmology & Visual Science 64 8 (2023) 385\u2013385."},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"Dinggang Shen Guorong Wu and Heung-Il Suk. 2017. Deep learning in medical image analysis. Annual review of biomedical engineering 19 (2017) 221\u2013248.","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Hyewon Song Anh-Duc Nguyen Myoungsik Gong and Sanghoon Lee. 2016. A review of computer vision methods for purpose on computer-aided diagnosis. Journal of International Society for Simulation Surgery 3 1 (2016) 1\u20138.","DOI":"10.18204\/JISSiS.2016.3.1.001"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Joseph\u00a0N Stember Haydar Celik E Krupinski Peter\u00a0D Chang Simukayi Mutasa Bradford\u00a0J Wood A Lignelli Gul Moonis LH Schwartz Sachin Jambawalikar et\u00a0al. 2019. Eye tracking for deep learning segmentation using convolutional neural networks. Journal of digital imaging 32 (2019) 597\u2013604.","DOI":"10.1007\/s10278-019-00220-4"},{"key":"e_1_3_3_2_37_2","unstructured":"B Thylefors and AD2486713 Negrel. 1994. The global impact of glaucoma. Bulletin of the World Health Organization 72 3 (1994) 323."},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Muhammad Usman Muhammad\u00a0Moazam Fraz and Sarah\u00a0A Barman. 2017. Computer vision techniques applied for diagnostic analysis of retinal OCT images: a review. Archives of Computational Methods in Engineering 24 (2017) 449\u2013465.","DOI":"10.1007\/s11831-016-9174-3"},{"key":"e_1_3_3_2_39_2","unstructured":"Sheng Wang Zihao Zhao Xi Ouyang Qian Wang and Dinggang Shen. 2023. Chatcad: Interactive computer-aided diagnosis on medical image using large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2302.07257 (2023)."},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Xi Wang Hao Chen An-Ran Ran Luyang Luo Poemen\u00a0P Chan Clement\u00a0C Tham Robert\u00a0T Chang Suria\u00a0S Mannil Carol\u00a0Y Cheung and Pheng-Ann Heng. 2020. Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning. Medical Image Analysis 63 (2020) 101695.","DOI":"10.1016\/j.media.2020.101695"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300468"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.5555\/3134594"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"crossref","unstructured":"Mohammad\u00a0JM Zedan Mohd\u00a0Asyraf Zulkifley Ahmad\u00a0Asrul Ibrahim Asraf\u00a0Mohamed Moubark Nor Azwan\u00a0Mohamed Kamari and Siti\u00a0Raihanah Abdani. 2023. Automated glaucoma screening and diagnosis based on retinal fundus images using deep learning approaches: a comprehensive review. Diagnostics 13 13 (2023) 2180.","DOI":"10.3390\/diagnostics13132180"}],"event":{"name":"CHI EA '25: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems","location":"Yokohama Japan","acronym":"CHI EA '25","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706599.3719719","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706599.3719719","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:43Z","timestamp":1750295923000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706599.3719719"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":42,"alternative-id":["10.1145\/3706599.3719719","10.1145\/3706599"],"URL":"https:\/\/doi.org\/10.1145\/3706599.3719719","relation":{},"subject":[],"published":{"date-parts":[[2025,4,25]]},"assertion":[{"value":"2025-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}