{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:31:35Z","timestamp":1767141095277,"version":"build-2238731810"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00521-023-09363-6","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T04:02:18Z","timestamp":1705377738000},"page":"5889-5905","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Aggregating efficient transformer and CNN networks using learnable fuzzy measure for breast tumor malignancy prediction in ultrasound images"],"prefix":"10.1007","volume":"36","author":[{"given":"Vivek Kumar","family":"Singh","sequence":"first","affiliation":[]},{"given":"Ehab Mahmoud","family":"Mohamed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1074-2441","authenticated-orcid":false,"given":"Mohamed","family":"Abdel-Nasser","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"issue":"7","key":"9363_CR1","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.3390\/s17071572","volume":"17","author":"L Wang","year":"2017","unstructured":"Wang L (2017) Early diagnosis of breast cancer. Sensors 17(7):1572","journal-title":"Sensors"},{"issue":"3","key":"9363_CR2","first-page":"458","volume":"95","author":"SW Duffy","year":"2002","unstructured":"Duffy SW, Tab\u00e1r L, Chen H-H, Holmqvist M, Yen M-F, Abdsalah S, Epstein B, Frodis E, Ljungberg E, Hedborg-Melander C et al (2002) The impact of organized mammography service screening on breast carcinoma mortality in seven Swedish counties: a collaborative evaluation. Cancer Interdiscip Int J Am Cancer Soc 95(3):458\u2013469","journal-title":"Cancer Interdiscip Int J Am Cancer Soc"},{"key":"9363_CR3","doi-asserted-by":"crossref","unstructured":"Asiedu MN, Benjamin AR, Singh VK, Wang S, Wu K, Samir AE, Kumar VS (2022) A generative adversarial network for ultrasound signal enhancement by transforming low-voltage beamformed radio frequency data to high-voltage data. In: Medical Imaging 2022: Ultrasonic Imaging and Tomography, vol 12038, pp 246\u2013254. SPIE","DOI":"10.1117\/12.2612686"},{"key":"9363_CR4","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"9363_CR5","doi-asserted-by":"crossref","unstructured":"Shamshad F, Khan S, Zamir SW, Khan MH. Hayat M, Khan FS, Fu H (2022) Transformers in medical imaging: a survey. arXiv preprint arXiv:2201.09873","DOI":"10.1016\/j.media.2023.102802"},{"issue":"10","key":"9363_CR6","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.3390\/diagnostics11101859","volume":"11","author":"EY Kalafi","year":"2021","unstructured":"Kalafi EY, Jodeiri A, Setarehdan SK, Lin NW, Rahmat K, Taib NA, Ganggayah MD, Dhillon SK (2021) Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics 11(10):1859","journal-title":"Diagnostics"},{"key":"9363_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108427","volume":"124","author":"Y Luo","year":"2022","unstructured":"Luo Y, Huang Q, Li X (2022) Segmentation information with attention integration for classification of breast tumor in ultrasound image. Pattern Recogn 124:108427","journal-title":"Pattern Recogn"},{"key":"9363_CR8","doi-asserted-by":"crossref","unstructured":"Gheflati B, Rivaz H (2021) Vision transformer for classification of breast ultrasound images. arXiv preprint arXiv:2110.14731","DOI":"10.1109\/EMBC48229.2022.9871809"},{"issue":"5","key":"9363_CR9","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.3390\/diagnostics12051053","volume":"12","author":"MA Hassanien","year":"2022","unstructured":"Hassanien MA, Singh VK, Puig D, Abdel-Nasser M (2022) Predicting breast tumor malignancy using deep convnext radiomics and quality-based score pooling in ultrasound sequences. Diagnostics 12(5):1053","journal-title":"Diagnostics"},{"issue":"23","key":"9363_CR10","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab5093","volume":"64","author":"H Tanaka","year":"2019","unstructured":"Tanaka H, Chiu S-W, Watanabe T, Kaoku S, Yamaguchi T (2019) Computer-aided diagnosis system for breast ultrasound images using deep learning. Phys Med Biol 64(23):235013","journal-title":"Phys Med Biol"},{"issue":"1","key":"9363_CR11","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"9363_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2022.106744","volume":"124","author":"A Chowdhury","year":"2022","unstructured":"Chowdhury A, Razzaque RR, Muhtadi S, Shafiullah A, Abir EUI, Garra BS, Alam SK (2022) Ultrasound classification of breast masses using a comprehensive Nakagami imaging and machine learning framework. Ultrasonics 124:106744","journal-title":"Ultrasonics"},{"key":"9363_CR13","doi-asserted-by":"crossref","unstructured":"Wei M, Du Y, Wu X, Su Q, Zhu J, Zheng L, Lv G, Zhuang J (2020) A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images. Comput Math Methods Med 2020","DOI":"10.1155\/2020\/5894010"},{"issue":"9","key":"9363_CR14","doi-asserted-by":"publisher","first-page":"4112","DOI":"10.1002\/mp.13082","volume":"45","author":"H Nemat","year":"2018","unstructured":"Nemat H, Fehri H, Ahmadinejad N, Frangi AF, Gooya A (2018) Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Med Phys 45(9):4112\u20134124","journal-title":"Med Phys"},{"key":"9363_CR15","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.engappai.2016.12.019","volume":"59","author":"M Abdel-Nasser","year":"2017","unstructured":"Abdel-Nasser M, Melendez J, Moreno A, Omer OA, Puig D (2017) Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell 59:84\u201392","journal-title":"Eng Appl Artif Intell"},{"key":"9363_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102828","volume":"69","author":"M Byra","year":"2021","unstructured":"Byra M (2021) Breast mass classification with transfer learning based on scaling of deep representations. Biomed Signal Process Control 69:102828. https:\/\/doi.org\/10.1016\/j.bspc.2021.102828","journal-title":"Biomed Signal Process Control"},{"key":"9363_CR17","doi-asserted-by":"publisher","first-page":"7733583","DOI":"10.1155\/2022\/7733583","volume":"2022","author":"R Du","year":"2022","unstructured":"Du R, Chen Y, Li T, Shi L, Fei Z, Li Y (2022) Discrimination of breast cancer based on ultrasound images and convolutional neural network. J Oncol 2022:7733583","journal-title":"J Oncol"},{"issue":"19","key":"9363_CR18","doi-asserted-by":"publisher","first-page":"7714","DOI":"10.1088\/1361-6560\/aa82ec","volume":"62","author":"S Han","year":"2017","unstructured":"Han S, Kang H-K, Jeong J-Y, Park M-H, Kim W, Bang W-C, Seong Y-K (2017) A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 62(19):7714","journal-title":"Phys Med Biol"},{"key":"9363_CR19","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.media.2018.12.006","volume":"52","author":"X Qi","year":"2019","unstructured":"Qi X, Zhang L, Chen Y, Pi Y, Chen Y, Lv Q, Yi Z (2019) Automated diagnosis of breast ultrasonography images using deep neural networks. Med Image Anal 52:185\u2013198","journal-title":"Med Image Anal"},{"key":"9363_CR20","unstructured":"Ge S, Ye Q, Xie W, Sun D, Zhang H, Zhou X, Yuan K (2021) AI assisted method for efficiently generating breast ultrasound screening reports. arXiv preprint arXiv:2107.13431"},{"key":"9363_CR21","doi-asserted-by":"crossref","unstructured":"Mo Y, Han C, Liu Y, Liu M, Shi Z, Lin J, Zhao B, Huang C, Qiu B, Cui Y, et al (2022) Hover-trans: Anatomy-aware hover-transformer for ROI-free breast cancer diagnosis in ultrasound images. arXiv preprint arXiv:2205.08390","DOI":"10.1109\/TMI.2023.3236011"},{"key":"9363_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105361","volume":"190","author":"WK Moon","year":"2020","unstructured":"Moon WK, Lee Y-W, Ke H-H, Lee SH, Huang C-S, Chang R-F (2020) Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Comput Methods Programs Biomed 190:105361","journal-title":"Comput Methods Programs Biomed"},{"issue":"11","key":"9363_CR23","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.diii.2021.09.002","volume":"102","author":"E Evain","year":"2021","unstructured":"Evain E, Raynaud C, Ciofolo-Veit C, Popoff A, Caramella T, Kbaier P, Balleyguier C, Harguem-Zayani S, Dapvril H, Ceugnart L et al (2021) Breast nodule classification with two-dimensional ultrasound using mask-RCNN ensemble aggregation. Diagn Interv Imaging 102(11):653\u2013658","journal-title":"Diagn Interv Imaging"},{"issue":"3","key":"9363_CR24","doi-asserted-by":"publisher","first-page":"439","DOI":"10.3390\/biology11030439","volume":"11","author":"M Ragab","year":"2022","unstructured":"Ragab M, Albukhari A, Alyami J, Mansour RF (2022) Ensemble deep-learning-enabled clinical decision support system for breast cancer diagnosis and classification on ultrasound images. Biology 11(3):439","journal-title":"Biology"},{"key":"9363_CR25","unstructured":"Misra S, Jeon S, Managuli R, Lee S, Kim G, Lee S, Barr RG, Kim C (2021) Ensemble transfer learning of elastography and B-mode breast ultrasound images. arXiv preprint arXiv:2102.08567"},{"key":"9363_CR26","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"9363_CR27","doi-asserted-by":"crossref","unstructured":"Chen C-FR, Fan Q, Panda R (2021) Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 357\u2013366","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"9363_CR28","first-page":"20014","volume":"34","author":"A Ali","year":"2021","unstructured":"Ali A, Touvron H, Caron M, Bojanowski P, Douze M, Joulin A, Laptev I, Neverova N, Synnaeve G, Verbeek J et al (2021) Xcit: cross-covariance image transformers. Adv Neural Inf Process Syst 34:20014\u201320027","journal-title":"Adv Neural Inf Process Syst"},{"key":"9363_CR29","first-page":"9355","volume":"34","author":"X Chu","year":"2021","unstructured":"Chu X, Tian Z, Wang Y, Zhang B, Ren H, Wei X, Xia H, Shen C (2021) Twins: revisiting the design of spatial attention in vision transformers. Adv Neural Inf Process Syst 34:9355\u20139366","journal-title":"Adv Neural Inf Process Syst"},{"key":"9363_CR30","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.inffus.2019.10.005","volume":"57","author":"GP Dimuro","year":"2020","unstructured":"Dimuro GP, Fern\u00e1ndez J, Bedregal B, Mesiar R, Sanz JA, Lucca G, Bustince H (2020) The state-of-art of the generalizations of the Choquet integral: from aggregation and pre-aggregation to ordered directionally monotone functions. Information Fusion 57:27\u201343","journal-title":"Information Fusion"},{"key":"9363_CR31","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"issue":"8","key":"9363_CR32","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to roc analysis. Pattern Recogn Lett 27(8):861\u2013874","journal-title":"Pattern Recogn Lett"},{"issue":"4","key":"9363_CR33","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","volume":"22","author":"MH Yap","year":"2017","unstructured":"Yap MH, Pons G, Mart\u00ed J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R (2017) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22(4):1218\u20131226","journal-title":"IEEE J Biomed Health Inform"},{"key":"9363_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data Brief 28:104863","journal-title":"Data Brief"},{"key":"9363_CR35","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9363_CR36","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9363_CR37","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"9363_CR38","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"9363_CR39","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"9363_CR40","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"9363_CR41","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp 6105\u20136114. PMLR"},{"issue":"10","key":"9363_CR42","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X et al (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349\u20133364","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9363_CR43","unstructured":"Tan M, Le Q (2021) Efficientnetv2: Smaller models and faster training. In: International conference on machine learning, pp 10096\u201310106. PMLR"},{"key":"9363_CR44","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"9363_CR45","doi-asserted-by":"crossref","unstructured":"Chen Z, Xie L, Niu J, Liu X, Wei L, Tian Q (2021) Visformer: the vision-friendly transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 589\u2013598","DOI":"10.1109\/ICCV48922.2021.00063"},{"issue":"2","key":"9363_CR46","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1002\/mp.13361","volume":"46","author":"M Byra","year":"2019","unstructured":"Byra M, Galperin M, Ojeda-Fournier H, Olson L, O\u2019Boyle M, Comstock C, Andre M (2019) Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys 46(2):746\u2013755","journal-title":"Med Phys"},{"key":"9363_CR47","unstructured":"Byra M, Andre M (2019) Breast mass classification in ultrasound based on Kendall\u2019s shape manifold. arXiv preprint arXiv:1905.11159"},{"key":"9363_CR48","doi-asserted-by":"crossref","unstructured":"Ning Z, Tu C, Xiao Q, Luo J, Zhang Y (2020) Multi-scale gradational-order fusion framework for breast lesions classification using ultrasound images. In: International conference on medical image computing and computer-assisted intervention, pp 171\u2013180. Springer","DOI":"10.1007\/978-3-030-59725-2_17"},{"key":"9363_CR49","doi-asserted-by":"crossref","unstructured":"Das A, Rana S (2021) Exploring residual networks for breast cancer detection from ultrasound images. In: 2021 12th international conference on computing communication and networking technologies (ICCCNT). IEEE, pp 1\u20136","DOI":"10.1109\/ICCCNT51525.2021.9580160"},{"issue":"11","key":"9363_CR50","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.3390\/cancers14112663","volume":"14","author":"N Vigil","year":"2022","unstructured":"Vigil N, Barry M, Amini A, Akhloufi M, Maldague XP, Ma L, Ren L, Yousefi B (2022) Dual-intended deep learning model for breast cancer diagnosis in ultrasound imaging. Cancers 14(11):2663","journal-title":"Cancers"}],"updated-by":[{"DOI":"10.1007\/s00521-024-09571-8","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09363-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09363-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09363-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T07:08:41Z","timestamp":1710832121000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09363-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":50,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9363"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09363-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]},"assertion":[{"value":"22 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-024-09571-8","URL":"https:\/\/doi.org\/10.1007\/s00521-024-09571-8","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}