{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T20:52:39Z","timestamp":1781556759500,"version":"3.54.5"},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"RFIER-Jio Institute","award":["2022\/33185004"],"award-info":[{"award-number":["2022\/33185004"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"DOI":"10.1007\/s13721-023-00437-y","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T17:02:03Z","timestamp":1703264523000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead"],"prefix":"10.1007","volume":"13","author":[{"given":"Sudipta","family":"Roy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debojyoti","family":"Pal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4197-2549","authenticated-orcid":false,"given":"Tanushree","family":"Meena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"437_CR1","doi-asserted-by":"crossref","unstructured":"Abeyagunasekera SHP, Perera Y, Chamara K, Kaushalya U, Sumathipala P, Senaweera O (2022) LISA: Enhance the explainability of medical images unifying current XAI techniques. In Proceedings of the 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), Mumbai, India, 7\u20139 April 2022; pp. 1\u20139","DOI":"10.1109\/I2CT54291.2022.9824840"},{"key":"437_CR2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5140148","author":"WH Abir","year":"2022","unstructured":"Abir WH, Uddin MF, Khanam FR, Tazin T, Khan MM, Masud M, Aljahdali S (2022) Explainable AI in diagnosing and anticipating leukemia using transfer learning method. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2022\/5140148","journal-title":"Comput Intell Neurosci"},{"key":"437_CR3","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6:52138\u201352160","journal-title":"IEEE Access"},{"key":"437_CR4","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s41598-021-04608-7","volume":"12","author":"B Alsinglawi","year":"2022","unstructured":"Alsinglawi B, Alshari O, Alorjani M, Mubin O, Alnajjar F, Novoa M, Darwish O (2022) An explainable machine learning framework for lung cancer hospital length of stay prediction. Sci Rep 12:607","journal-title":"Sci Rep"},{"key":"437_CR5","unstructured":"Ancona M, Ceolini E, \u00d6ztireli C, Gross M (2017) \u201cTowards better understanding of gradient-based attribution methods for deep neural networks.\u201d arXiv preprint arXiv:1711.06104"},{"key":"437_CR6","doi-asserted-by":"crossref","unstructured":"Arun N, Gaw N, Singh P, Chang K, Aggarwal M, Chen B (2020) \u201cAssessing the (Un) trustworthiness of saliency maps for localizing abnormalities in medical imaging. arXiv.\u201d arXiv preprint arXiv:2008.02766","DOI":"10.1101\/2020.07.28.20163899"},{"key":"437_CR7","doi-asserted-by":"publisher","first-page":"106156","DOI":"10.1016\/j.compbiomed.2022.106156","volume":"150","author":"M Bhandari","year":"2022","unstructured":"Bhandari M, Shahi TB, Siku B, Neupane A (2022) Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Comput Biol Med 150:106156","journal-title":"Comput Biol Med"},{"key":"437_CR8","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3389\/fnagi.2019.00194","volume":"11","author":"M B\u00f6hle","year":"2019","unstructured":"B\u00f6hle M, Eitel F, Weygandt M, Ritter K (2019) Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer\u2019s disease classification. Front Aging Neurosci 11:194","journal-title":"Front Aging Neurosci"},{"key":"437_CR9","doi-asserted-by":"publisher","first-page":"672","DOI":"10.3390\/app11020672","volume":"11","author":"J Born","year":"2021","unstructured":"Born J, Wiedemann N, Cossio M, Buhre C, Br\u00e4ndle G, Leidermann K, Goulet J, Aujayeb A, Moor M, Rieck B et al (2021) Accelerating detection of lung pathologies with explainable ultrasound image analysis. Appl Sci 11:672","journal-title":"Appl Sci"},{"key":"437_CR10","doi-asserted-by":"publisher","unstructured":"Chakraborty S, Kumar K, Reddy BP, Meena T, Roy S (2023) An Explainable AI based Clinical Assistance Model for Identifying Patients with the Onset of Sepsis,\u201d 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI), Bellevue, WA, USA pp. 297\u2013302. https:\/\/doi.org\/10.1109\/IRI58017.2023.00059","DOI":"10.1109\/IRI58017.2023.00059"},{"key":"437_CR11","doi-asserted-by":"crossref","unstructured":"Clough JR, Oksuz I, Puyol-Ant\u00f3n E, Ruijsink B, King AP, Schnabel JA (2019) \u201cGlobal and local interpretability for cardiac MRI classification.\u201d In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part IV 22, pp. 656\u2013664. Springer International Publishing","DOI":"10.1007\/978-3-030-32251-9_72"},{"key":"437_CR12","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-030-02628-8_11","volume-title":"Understanding and interpreting machine learning in medical image computing applications","author":"NC Codella","year":"2018","unstructured":"Codella NC, Lin CC, Halpern A, Hind M, Feris R, Smith JR (2018) Collaborative human-AI (CHAI): evidence-based interpretable melanoma classification in dermoscopic images. Understanding and interpreting machine learning in medical image computing applications. Springer, Cham, pp 97\u2013105"},{"key":"437_CR13","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-030-33850-3_7","volume-title":"Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support","author":"V Couteaux","year":"2019","unstructured":"Couteaux V, Nempont O, Pizaine G, Bloch I (2019) Towards interpretability of segmentation networks by analyzing DeepDreams. Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support. Springer, Cham, pp 56\u201363"},{"key":"437_CR14","doi-asserted-by":"crossref","unstructured":"Duell J, Fan X, Burnett B, Aarts G, Zhou SMA (2021) Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records. In Proceedings of the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece, 27\u201330 July 2021; pp. 1\u20134","DOI":"10.1109\/BHI50953.2021.9508618"},{"key":"437_CR15","doi-asserted-by":"crossref","unstructured":"Eitel F, Ritter K (2019) Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer\u2019s Disease Classification. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, ML-CDS 2019, IMIMIC 2019; Lecture Notes in Computer Science; Suzuki, K., et al., Eds.; Springer: Cham, Switzerland, 2019; Volume 11797","DOI":"10.1007\/978-3-030-33850-3_1"},{"key":"437_CR16","doi-asserted-by":"crossref","unstructured":"Fan Z, Gong P, Tang S, Lee CU, Zhang X, Song P, Chen S, Li H (2022) Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework. arXiv 2022. arXiv:2210.05762","DOI":"10.1016\/j.media.2023.102960"},{"key":"437_CR17","doi-asserted-by":"crossref","unstructured":"Fong RC, Vedaldi A (2017) \u201cInterpretable explanations of black boxes by meaningful perturbation.\u201d In Proceedings of the IEEE international conference on computer vision, pp. 3429\u20133437","DOI":"10.1109\/ICCV.2017.371"},{"key":"437_CR18","doi-asserted-by":"publisher","first-page":"3507","DOI":"10.1109\/JBHI.2021.3059453","volume":"25","author":"X Fu","year":"2021","unstructured":"Fu X, Bi L, Kumar A, Fulham M, Kim J (2021) Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation. IEEE J Biomed Health Inf 25:3507\u20133516","journal-title":"IEEE J Biomed Health Inf"},{"key":"437_CR19","doi-asserted-by":"publisher","unstructured":"Gao K, Shen H, Liu Y, Zeng L, Hu D (2019) \u201cDense-CAM: Visualize the Gender of Brains with MRI Images,\u201d 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, pp. 1\u20137. https:\/\/doi.org\/10.1109\/IJCNN.2019.8852260","DOI":"10.1109\/IJCNN.2019.8852260"},{"key":"437_CR20","doi-asserted-by":"publisher","DOI":"10.1080\/00365521.2022.2163185","author":"Z Ge","year":"2023","unstructured":"Ge Z, Wang B, Chang J, Yu Z, Zhou Z, Zhang J, Duan Z (2023) Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System. Scand J Gastroenterol. https:\/\/doi.org\/10.1080\/00365521.2022.2163185. (Epub ahead of print. PMID: 36625026)","journal-title":"Scand J Gastroenterol"},{"key":"437_CR21","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi M, Oakden-Rayner L, Beam AL (2021) The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 3:e745\u2013e750","journal-title":"Lancet Digit Health"},{"key":"437_CR22","doi-asserted-by":"crossref","unstructured":"Giuste F, Shi W, Zhu Y, Naren T, Isgut M, Sha Y, Tong L, Gupte M, Wang MD (2022) Explainable artificial intelligence methods in combating pandemics: a systematic review. IEEE Reviews in Biomedical Engineering, vol. XX, no. X","DOI":"10.1109\/RBME.2022.3185953"},{"issue":"9","key":"437_CR23","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.3390\/diagnostics12092084","volume":"12","author":"N Gozzi","year":"2022","unstructured":"Gozzi N, Giacomello E, Sollini M, Kirienko M, Ammirabile A, Lanzi P, Loiacono D, Chiti A (2022) Image embeddings extracted from CNNs outperform other transfer learning approaches in classification of chest radiographs. Diagnostics (basel) 12(9):2084. https:\/\/doi.org\/10.3390\/diagnostics12092084. (PMID:36140486;PMCID:PMC9497580)","journal-title":"Diagnostics (basel)"},{"key":"437_CR24","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/978-3-030-02628-8_14","volume-title":"Understanding and interpreting machine learning in medical image computing applications","author":"M Graziani","year":"2018","unstructured":"Graziani M, Andrearczyk V, M\u00fcller H (2018) Regression concept vectors for bidirectional explanations in histopathology. Understanding and interpreting machine learning in medical image computing applications. Springer, Cham, pp 124\u2013132"},{"key":"437_CR25","doi-asserted-by":"publisher","first-page":"30615","DOI":"10.1007\/s11042-022-12156-z","volume":"81","author":"A Haghanifar","year":"2022","unstructured":"Haghanifar A, Majdabadi MM, Choi Y, Deivalakshmi S, Ko S (2022) COVID-cxnet: detecting COVID-19 in frontal chest X-ray images using deep learning. Multimed Tools Appl 81:30615\u201330645","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"437_CR26","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.1287\/mnsc.2020.3595","volume":"67","author":"T-H Ho","year":"2021","unstructured":"Ho T-H, Park S-E, Xuanming Su (2021) A bayesian level-k model in n-person games. Manag Sci 67(3):1622\u20131638","journal-title":"Manag Sci"},{"key":"437_CR27","doi-asserted-by":"publisher","unstructured":"Hu B, Vasu B, Hoogs A (2022) \u201cX-MIR: EXplainable Medical Image Retrieval,\u201d 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2022, pp. 1544\u20131554, doi: https:\/\/doi.org\/10.1109\/WACV51458.2022.00161","DOI":"10.1109\/WACV51458.2022.00161"},{"key":"437_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104425","volume":"134","author":"G Jia","year":"2021","unstructured":"Jia G, Lam HK, Xu Y (2021) Classification of COVID-19 chest X-ray and CT images using a type of dynamic CNN modification method. Comput Biol Med 134:104425","journal-title":"Comput Biol Med"},{"key":"437_CR29","doi-asserted-by":"crossref","unstructured":"Jiang H, Yang K, Gao M, Zhang D, Ma H, Qian W (2019) \u201cAn interpretable ensemble deep learning model for diabetic retinopathy disease classification.\u201d In 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 2045\u20132048. IEEE","DOI":"10.1109\/EMBC.2019.8857160"},{"key":"437_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2023.102009","author":"W Jin","year":"2023","unstructured":"Jin W, Li X, Fatehi M, Hamarneh G (2023) Generating post-hoc explanation from deep neural networks for multi-modal medical image analysis tasks. MethodsX. https:\/\/doi.org\/10.1016\/j.mex.2023.102009","journal-title":"MethodsX"},{"key":"437_CR31","unstructured":"Jogani V, Purohit J, Shivhare I, Shrawne SC (2022) \u201cAnalysis of Explainable Artificial Intelligence Methods on Medical Image Classification.\u201d arXiv preprint arXiv:2212.10565"},{"key":"437_CR32","doi-asserted-by":"crossref","unstructured":"Kabiraj A, Meena T, Reddy PB, Roy S (2022) \u201cDetection and Classification of Lung Disease Using Deep Learning Architecture from X-ray Images.\u201d In Advances in Visual Computing: 17th International Symposium, ISVC 2022, San Diego, CA, USA, October 3\u20135, 2022, Proceedings, Part I, pp. 444\u2013455. Cham: Springer International Publishing","DOI":"10.1007\/978-3-031-20713-6_34"},{"key":"437_CR33","unstructured":"Kim B, Wattenberg M, Gilmer J, Cai C, Wexler J, Viegas F, Sayres R (2017) Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). arXiv 2017. arXiv:1711.11279"},{"key":"437_CR34","doi-asserted-by":"crossref","unstructured":"Kim ST, Lee JH, Ro YM (2019) \u201cVisual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis.\u201d In Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, pp. 139\u2013147. SPIE","DOI":"10.1117\/12.2512621"},{"issue":"1","key":"437_CR35","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1038\/s41467-022-29437-8","volume":"13","author":"D Kim","year":"2022","unstructured":"Kim D, Chung J, Choi J, Succi MD, Conklin J, Longo MGF, Ackman JB, Little BP, Petranovic M, Kalra MK, Lev MH, Do S (2022) Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model. Nat Commun 13(1):1867. https:\/\/doi.org\/10.1038\/s41467-022-29437-8. (PMID:35388010;PMCID:PMC8986787)","journal-title":"Nat Commun"},{"issue":"6","key":"437_CR36","doi-asserted-by":"publisher","first-page":"318","DOI":"10.3390\/info11060318","volume":"11","author":"K Kowsari","year":"2020","unstructured":"Kowsari K, Sali R, Ehsan L, Adorno W, Ali A, Moore S, Amadi B, Kelly P, Syed S, Brown D (2020) Hmic: Hierarchical medical image classification, a deep learning approach. Information 11(6):318","journal-title":"Information"},{"key":"437_CR37","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/j.jbusres.2020.10.030","volume":"123","author":"S Kraus","year":"2021","unstructured":"Kraus S, Schiavone F, Pluzhnikova A, Invernizzi AC (2021) Digital transformation in healthcare: analyzing the current state-of-research. J Bus Res 123:557\u2013567","journal-title":"J Bus Res"},{"key":"437_CR38","unstructured":"L\u00e9vy D, Jain A (2016) Breast mass classification from mammograms using deep convolutional neural networks. arXiv 2016. arXiv:1612.00542"},{"key":"437_CR39","doi-asserted-by":"publisher","unstructured":"Liao L et al. (2020) \u201cMulti-branch deformable convolutional neural network with label distribution learning for fetal brain age prediction,\u201d 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 424-427. https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098553.","DOI":"10.1109\/ISBI45749.2020.9098553"},{"key":"437_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101548","volume":"58","author":"Z Lin","year":"2019","unstructured":"Lin Z, Li S, Ni D, Liao Y, Wen H, Jie Du, Chen S, Wang T, Lei B (2019) Multi-task learning for quality assessment of fetal head ultrasound images. Med Image Anal 58:101548","journal-title":"Med Image Anal"},{"key":"437_CR41","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1002\/int.22686","volume":"37","author":"S Lu","year":"2022","unstructured":"Lu S, Zhu Z, Gorriz JM, Wang SH, Zhang YD (2022) NAGNN: Classification of COVID-19 based on neighboring aware representation from deep graph neural network. Int J Intell Syst 37:1572\u20131598","journal-title":"Int J Intell Syst"},{"key":"437_CR42","doi-asserted-by":"publisher","first-page":"106620","DOI":"10.1016\/j.cmpb.2022.106620","volume":"215","author":"A Lucieri","year":"2022","unstructured":"Lucieri A, Bajwa MN, Braun SA, Malik MI, Dengel A, Ahmed S (2022) ExAID: a multimodal explanation framework for computer-aided diagnosis of skin lesions. Comput Methods Programs Biomed 215:106620","journal-title":"Comput Methods Programs Biomed"},{"key":"437_CR43","doi-asserted-by":"crossref","unstructured":"Malhi A, Kampik T, Pannu H, Madhikermi M, Fr\u00e4mling K (2019) \u201cExplaining machine learning-based classifications of in-vivo gastral images.\u201d In 2019 Digital Image Computing: Techniques and Applications (DICTA), pp. 1\u20137. IEEE","DOI":"10.1109\/DICTA47822.2019.8945986"},{"key":"437_CR44","doi-asserted-by":"publisher","unstructured":"Meena T, Kabiraj A, Reddy PB, Roy S (2023) \u201cWeakly Supervised Confidence Aware Probabilistic CAM multi-Thorax Anomaly Localization Network,\u201d 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI), Bellevue, WA, USA, pp. 309\u2013314. https:\/\/doi.org\/10.1109\/IRI58017.2023.00061.","DOI":"10.1109\/IRI58017.2023.00061"},{"issue":"10","key":"437_CR45","doi-asserted-by":"publisher","first-page":"2420","DOI":"10.3390\/diagnostics12102420","volume":"12","author":"T Meena","year":"2022","unstructured":"Meena T, Roy S (2022) Bone fracture detection using deep supervised learning from radiological images: a paradigm shift. Diagnostics 12(10):2420","journal-title":"Diagnostics"},{"issue":"18","key":"437_CR46","doi-asserted-by":"publisher","first-page":"21544","DOI":"10.1109\/JSEN.2023.3301187","volume":"23","author":"T Meena","year":"2023","unstructured":"Meena T, Sarawadekar K (2023) Seq2Dense U-Net: analyzing sequential inertial sensor data for human activity recognition using dense segmentation model. IEEE Sens J 23(18):21544\u201321552. https:\/\/doi.org\/10.1109\/JSEN.2023.3301187","journal-title":"IEEE Sens J"},{"key":"437_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16783-y","author":"H Meswal","year":"2023","unstructured":"Meswal H, Kumar D, Gupta A et al (2023) A weighted ensemble transfer learning approach for melanoma classification from skin lesion images. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-16783-y","journal-title":"Multimed Tools Appl"},{"key":"437_CR48","unstructured":"Miwa D, Duy VN, Takeuchi I (2023) \u201cValid P-value for deep learning-driven salient region.\u201d arXiv preprint arXiv:2301.02437"},{"key":"437_CR49","doi-asserted-by":"publisher","first-page":"6968","DOI":"10.1038\/s41598-021-86327-7","volume":"11","author":"A Moncada-Torres","year":"2021","unstructured":"Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G (2021) Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep 11:6968","journal-title":"Sci Rep"},{"key":"437_CR50","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","volume":"65","author":"G Montavon","year":"2017","unstructured":"Montavon G, Lapuschkin S, Binder A, Samek W, M\u00fcller K-R (2017) Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recogn 65:211\u2013222","journal-title":"Pattern Recogn"},{"issue":"11","key":"437_CR51","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00007","volume":"2","author":"C Olah","year":"2017","unstructured":"Olah C, Mordvintsev A, Schubert L (2017) Feature visualization. Distill 2(11):e7","journal-title":"Distill"},{"key":"437_CR52","doi-asserted-by":"publisher","first-page":"106083","DOI":"10.1016\/j.compbiomed.2022.106083","volume":"150","author":"D Pal","year":"2022","unstructured":"Pal D, Reddy PB, Roy S (2022) Attention UW-Net: a fully connected model for automatic segmentation and annotation of chest X-ray. Comput Biol Med 150:106083","journal-title":"Comput Biol Med"},{"key":"437_CR53","doi-asserted-by":"publisher","unstructured":"Pal D, Meena T, Roy S (2023) \u201cA fully connected reproducible SE-UResNet for multiorgan chest radiographs segmentation,\u201d 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI), Bellevue, WA, USA, 2023, pp. 261\u2013266, doi: https:\/\/doi.org\/10.1109\/IRI58017.2023.00052","DOI":"10.1109\/IRI58017.2023.00052"},{"key":"437_CR54","doi-asserted-by":"crossref","unstructured":"Papanastasopoulos Z, Samala RK, Chan HP, Hadjiiski L, Paramagul C, Helvie MA, Neal CH (2020) Explainable AI for medical imaging: Deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI. In Proceedings of the SPIE Medical Imaging 2020: Computer-Aided Diagnosis; International Society for Optics and Photonics: Bellingham, WA, USA, 2020; Volume 11314, p. 113140Z","DOI":"10.1117\/12.2549298"},{"key":"437_CR55","unstructured":"Patr\u00edcio C, Neves JC, Teixeira LF. Explainable deep learning methods in medical imaging diagnosis: a survey. arXiv:2205.04766v2. [eess.IV] 13 Jun 2022"},{"key":"437_CR56","doi-asserted-by":"crossref","unstructured":"Peng T, Boxberg M, Weichert W, Navab N, Marr C (2019) \u201cMulti-task learning of a deep k-nearest neighbour network for histopathological image classification and retrieval.\u201d In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part I 22, pp. 676\u2013684. Springer International Publishing","DOI":"10.1007\/978-3-030-32239-7_75"},{"key":"437_CR57","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-030-02628-8_12","volume-title":"Understanding and interpreting machine learning in medical image computing applications","author":"S Pereira","year":"2018","unstructured":"Pereira S, Meier R, Alves V, Reyes M, Silva CA (2018) Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. Understanding and interpreting machine learning in medical image computing applications. Springer, Cham, pp 106\u2013114"},{"key":"437_CR58","unstructured":"Petsiuk V, Das A, Saenko K (2018) \u201cRise: randomized input sampling for explanation of black-box models.\u201d arXiv preprint arXiv:1806.07421"},{"key":"437_CR59","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1038\/s41591-020-01192-7","volume":"27","author":"E Pierson","year":"2021","unstructured":"Pierson E, Cutler DM, Leskovec J, Mullainathan S, Obermeyer Z (2021) An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat Med 27:136\u2013140","journal-title":"Nat Med"},{"key":"437_CR60","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-030-33850-3_4","volume-title":"Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support","author":"M Pisov","year":"2019","unstructured":"Pisov M, Goncharov M, Kurochkina N, Morozov S, Gombolevsky V, Chernina V, Vladzymyrskyy A, Zamyatina K, Cheskova A, Pronin I et al (2019) Incorporating task-specific structural knowledge into CNNs for brain midline shift detection. Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support. Springer, Cham, pp 30\u201338"},{"key":"437_CR61","doi-asserted-by":"publisher","first-page":"2689","DOI":"10.1007\/s10489-020-01900-3","volume":"51","author":"NS Punn","year":"2021","unstructured":"Punn NS, Agarwal S (2021) Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Appl Intell 51:2689\u20132702","journal-title":"Appl Intell"},{"key":"437_CR62","doi-asserted-by":"publisher","first-page":"102118","DOI":"10.1016\/j.media.2021.102118","volume":"72","author":"G Quellec","year":"2021","unstructured":"Quellec G, Al Hajj H, Lamard M, Conze PH, Massin P, Cochener B (2021) ExplAIn: explanatory artificial intelligence for diabetic retinopathy diagnosis. Med Image Anal 72:102118","journal-title":"Med Image Anal"},{"key":"437_CR63","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00322-2","author":"P Rajpurkar","year":"2020","unstructured":"Rajpurkar P, Oconnell C, Schechter A, Asnani N, Li J, Kiani A, Ball RL et al (2020) CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV. NPJ Dig Med. https:\/\/doi.org\/10.1038\/s41746-020-00322-2","journal-title":"NPJ Dig Med"},{"key":"437_CR64","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier.\u201d In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"key":"437_CR65","doi-asserted-by":"crossref","unstructured":"Roy S, Bandyopadhyay SK (2013) \u201cAbnormal regions detection and quantification with accuracy estimation from MRI of brain.\u201d In 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA), pp. 611\u2013615. IEEE","DOI":"10.1109\/IMSNA.2013.6743351"},{"key":"437_CR66","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.procs.2016.05.244","volume":"85","author":"S Roy","year":"2016","unstructured":"Roy S, Bandyopadhyay SK (2016) A new method of brain tissues segmentation from MRI with accuracy estimation. Procedia Comput Sci 85:362\u2013369","journal-title":"Procedia Comput Sci"},{"key":"437_CR67","doi-asserted-by":"crossref","unstructured":"Roy S, Shoghi KI (2019) \u201cComputer-aided tumor segmentation from T2-weighted MR images of patient-derived tumor xenografts.\u201d In Image Analysis and Recognition: 16th International Conference, ICIAR 2019, Waterloo, ON, Canada, August 27\u201329, 2019, Proceedings, Part II 16, pp. 159\u2013171. Springer International Publishing","DOI":"10.1007\/978-3-030-27272-2_14"},{"issue":"6","key":"437_CR68","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1080\/03772063.2017.1331757","volume":"63","author":"S Roy","year":"2017","unstructured":"Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH (2017a) An iterative implementation of level set for precise segmentation of brain tissues and abnormality detection from MR images. IETE J Res 63(6):769\u2013783","journal-title":"IETE J Res"},{"key":"437_CR69","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.cmpb.2017.01.003","volume":"140","author":"S Roy","year":"2017","unstructured":"Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH (2017b) An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI. Comput Methods Programs Biomed 140:307\u2013320. https:\/\/doi.org\/10.1016\/j.cmpb.2017.01.003","journal-title":"Comput Methods Programs Biomed"},{"key":"437_CR70","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.imu.2018.02.006","volume":"13","author":"S Roy","year":"2018","unstructured":"Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH (2018) Heterogeneity of human brain tumor with lesion identification, localization, and analysis from MRI. Inform Med Unlocked 13:139\u2013150","journal-title":"Inform Med Unlocked"},{"key":"437_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.102963","volume":"59","author":"S Roy","year":"2020","unstructured":"Roy S, Whitehead TD, Quirk JD, Salter A, Ademuyiwa FO, Li S, An H, Shoghi KI (2020) Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine 59:102963","journal-title":"EBioMedicine"},{"issue":"10","key":"437_CR72","doi-asserted-by":"publisher","first-page":"2549","DOI":"10.3390\/diagnostics12102549","volume":"12","author":"S Roy","year":"2022","unstructured":"Roy S, Meena T, Lim SJ (2022) Demystifying supervised learning in healthcare 4.0: a new reality of transforming diagnostic medicine. Diagnostics 12(10):2549","journal-title":"Diagnostics"},{"issue":"5","key":"437_CR73","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206\u2013215","journal-title":"Nat Mach Intell"},{"key":"437_CR74","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/978-3-030-28954-6_1","volume-title":"\u201cTowards explainable artificial intelligence\u201d. Explainable AI: interpreting, explaining and visualizing deep learning","author":"W Samek","year":"2019","unstructured":"Samek W, M\u00fcller KR (2019) \u201cTowards explainable artificial intelligence\u201d. Explainable AI: interpreting, explaining and visualizing deep learning. Springer, pp 5\u201322"},{"key":"437_CR75","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"Jo Schlemper","year":"2019","unstructured":"Schlemper Jo, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D (2019) Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53:197\u2013207","journal-title":"Med Image Anal"},{"key":"437_CR76","doi-asserted-by":"crossref","unstructured":"Schwab E, Goo\u00dfen A, Deshpande H, Saalbach A (2020) \u201cLocalization of critical findings in chest X-ray without local annotations using multi-instance learning.\u201d In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1879\u20131882. IEEE","DOI":"10.1109\/ISBI45749.2020.9098551"},{"key":"437_CR77","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) \u201cGrad-cam: Visual explanations from deep networks via gradient-based localization.\u201d In Proceedings of the IEEE international conference on computer vision, pp. 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"437_CR78","doi-asserted-by":"publisher","first-page":"8572","DOI":"10.1109\/ACCESS.2019.2963055","volume":"8","author":"D Seo","year":"2019","unstructured":"Seo D, Kanghan Oh, Il-Seok Oh (2019) Regional multi-scale approach for visually pleasing explanations of deep neural networks. IEEE Access 8:8572\u20138582","journal-title":"IEEE Access"},{"key":"437_CR79","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101908","volume":"68","author":"Y Shen","year":"2021","unstructured":"Shen Y, Wu N, Phang J, Park J, Liu K, Tyagi S, Heacock L, Kim SG, Moy L, Cho K et al (2021) An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Med Image Anal 68:101908","journal-title":"Med Image Anal"},{"issue":"16","key":"437_CR80","doi-asserted-by":"publisher","first-page":"1029784","DOI":"10.3389\/fnhum.2022.1029784","volume":"19","author":"CJ Shibu","year":"2023","unstructured":"Shibu CJ, Sreedharan S, Arun KM, Kesavadas C, Sitaram R (2023) Explainable artificial intelligence model to predict brain states from fNIRS signals. Front Hum Neurosci 19(16):1029784. https:\/\/doi.org\/10.3389\/fnhum.2022.1029784. (PMID:36741783;PMCID:PMC9892761)","journal-title":"Front Hum Neurosci"},{"key":"437_CR81","unstructured":"Shrikumar A, Greenside P, Kundaje A (2017) Learning important features through propagating activation differences. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6\u201311 August 2017; Voume 70, pp. 3145\u20133153"},{"key":"437_CR82","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/978-3-030-02628-8_15","volume-title":"Understanding and interpreting machine learning in medical image computing applications","author":"W Silva","year":"2018","unstructured":"Silva W, Fernandes K, Cardoso MJ, Cardoso JS (2018) Towards complementary explanations using deep neural networks. Understanding and interpreting machine learning in medical image computing applications. Springer, Cham, pp 133\u2013140"},{"key":"437_CR83","doi-asserted-by":"crossref","unstructured":"Singh S, Karimi S, Ho-Shon K, Hamey L (2019.) \u201cFrom chest x-rays to radiology reports: a multimodal machine learning approach.\u201d In 2019 Digital Image Computing: Techniques and Applications (DICTA), pp. 1\u20138. IEEE","DOI":"10.1109\/DICTA47822.2019.8945819"},{"issue":"6","key":"437_CR84","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/jimaging6060052","volume":"6","author":"A Singh","year":"2020","unstructured":"Singh A, Sengupta S, Lakshminarayanan V (2020) Explainable deep learning models in medical image analysis. J Imaging 6(6):52. https:\/\/doi.org\/10.3390\/jimaging6060052. (PMID:34460598;PMCID:PMC8321083)","journal-title":"J Imaging"},{"key":"437_CR85","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1109\/TCBB.2021.3065361","volume":"18","author":"Y Song","year":"2021","unstructured":"Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Wang R, Zhao H, Chong Y et al (2021) Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE\/ACM Trans Comput Biol Bioinform 18:2775\u20132780","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"437_CR86","unstructured":"Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2014) \u201cStriving for simplicity: The all convolutional net.\u201d arXiv preprint arXiv:1412.6806"},{"key":"437_CR87","doi-asserted-by":"crossref","unstructured":"Stano M, Benesova W, Martak LS (2019) Explainable 3D convolutional neural network using GMM encoding. In Proceedings of the Twelfth International Conference on Machine Vision, Amsterdam, The Netherlands, 16\u201318 November 2019; Volume 11433, p. 114331U.","DOI":"10.1117\/12.2557314"},{"key":"437_CR88","doi-asserted-by":"crossref","unstructured":"Sun J, Darbeha F, Zaidi M, Wang B (2020) SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation. arXiv 2020. arXiv:2001.07645","DOI":"10.1007\/978-3-030-59719-1_77"},{"key":"437_CR89","doi-asserted-by":"publisher","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","volume":"79","author":"BH Van der Velden","year":"2022","unstructured":"Van der Velden BH, Kuijf HJ, Gilhuijs KG, Viergever MA (2022) Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal 79:102470","journal-title":"Med Image Anal"},{"key":"437_CR90","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-030-02628-8_13","volume-title":"Understanding and interpreting machine learning in medical image computing applications","author":"P Van Molle","year":"2018","unstructured":"Van Molle P, Der Strooper M, Verbelen T, Vankeirsbilck B, Simoens P, Dhoedt B (2018) Visualizing convolutional neural networks to improve decision support for skin lesion classification. Understanding and interpreting machine learning in medical image computing applications. Springer, Cham, pp 115\u2013123"},{"key":"437_CR91","doi-asserted-by":"crossref","unstructured":"Wang L, Wong A (2020) COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv 2020. arXiv:2003.09871","DOI":"10.1038\/s41598-020-76550-z"},{"key":"437_CR92","doi-asserted-by":"publisher","unstructured":"Wang Z, Zhu H, Ma Y, Basu A (2021) \u201cXAI Feature Detector for Ultrasound Feature Matching,\u201d 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 2928\u20132931, https:\/\/doi.org\/10.1109\/EMBC46164.2021.9629944","DOI":"10.1109\/EMBC46164.2021.9629944"},{"key":"437_CR93","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.inffus.2020.10.004","volume":"67","author":"SH Wang","year":"2021","unstructured":"Wang SH, Govindaraj VV, G\u00f3rriz JM, Zhang X, Zhang YD (2021b) COVID-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf Fusion 67:208\u2013229","journal-title":"Inf Fusion"},{"key":"437_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107613","volume":"110","author":"Z Wang","year":"2021","unstructured":"Wang Z, Xiao Y, Li Y, Zhang J, Lu F, Hou M, Liu X (2021c) Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays. Pattern Recognit 110:107613","journal-title":"Pattern Recognit"},{"key":"437_CR95","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101846","volume":"67","author":"H Wang","year":"2021","unstructured":"Wang H, Wang S, Qin Z, Zhang Y, Li R, Xia Y (2021d) Triple attention learning for classification of 14 thoracic diseases using chest radiography. Med Image Anal 67:101846","journal-title":"Med Image Anal"},{"key":"437_CR96","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1007\/s00234-020-02465-1","volume":"62","author":"P Windisch","year":"2020","unstructured":"Windisch P, Weber P, F\u00fcrweger C, Ehret F, Kufeld M, Zwahlen D, Muacevic A (2020) Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology 62:1515\u20131518","journal-title":"Neuroradiology"},{"issue":"11","key":"437_CR97","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1007\/s00234-020-02465-1","volume":"62","author":"P Windisch","year":"2020","unstructured":"Windisch P et al (2020) Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology 62(11):1515\u20131518. https:\/\/doi.org\/10.1007\/s00234-020-02465-1","journal-title":"Neuroradiology"},{"key":"437_CR98","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","volume":"30","author":"YH Wu","year":"2021","unstructured":"Wu YH, Gao SH, Mei J, Xu J, Fan DP, Zhang RG, Cheng MM (2021) JCS: an explainable covid-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process 30:3113\u20133126","journal-title":"IEEE Trans Image Process"},{"key":"437_CR99","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s11548-020-02182-3","volume":"15","author":"B Xie","year":"2020","unstructured":"Xie B, Lei T, Wang N et al (2020) Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. Int J CARS 15:1303\u20131312. https:\/\/doi.org\/10.1007\/s11548-020-02182-3","journal-title":"Int J CARS"},{"issue":"11","key":"437_CR100","doi-asserted-by":"publisher","first-page":"8583","DOI":"10.1002\/int.22957","volume":"37","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Zhan D, Luo S, Dai P, Li K (2022a) SelfMatch: robust semisupervised time-series classification with self-distillation. Int J Intell Syst 37(11):8583\u20138610","journal-title":"Int J Intell Syst"},{"key":"437_CR101","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3201203)","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Rong Qu, Zhu Z, Zhao B (2022b) An efficient federated distillation learning system for multitask time series classification. IEEE Trans Instrum Meas. https:\/\/doi.org\/10.1109\/TIM.2022.3201203","journal-title":"IEEE Trans Instrum Meas"},{"issue":"8","key":"437_CR102","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.797616","volume":"14","author":"F Xu","year":"2021","unstructured":"Xu F, Jiang L, He W, Huang G, Hong Y, Tang F, Lv J, Lin Y, Qin Y, Lan R, Pan X, Zeng S, Li M, Chen Q, Tang N (2021) The clinical value of explainable deep learning for diagnosing fungal keratitis using in vivo confocal microscopy images. Front Med (lausanne) 14(8):797616. https:\/\/doi.org\/10.3389\/fmed.2021.797616. (PMID:34970572;PMCID:PMC8712475)","journal-title":"Front Med (lausanne)"},{"key":"437_CR103","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R, Nishio M, Do RKG et al (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611\u2013629. https:\/\/doi.org\/10.1007\/s13244-018-0639-9","journal-title":"Insights Imaging"},{"key":"437_CR104","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0215076","volume":"14","author":"HL Yang","year":"2019","unstructured":"Yang HL, Kim JJ, Kim JH, Kang YK, Park DH, Park HS, Kim HK, Kim MS (2019) Weakly supervised lesion localization for age-related macular degeneration detection using optical coherence tomography images. PLoS ONE 14:e0215076","journal-title":"PLoS ONE"},{"key":"437_CR105","doi-asserted-by":"crossref","unstructured":"Yang G, Ye Q, Xia J (2021) \u201cUnbox the Black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond\u201d. arXiv:2102.01998v1 [cs.AI] 3 Feb 2021","DOI":"10.1016\/j.inffus.2021.07.016"},{"key":"437_CR106","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-030-33850-3_2","volume-title":"Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support","author":"H Yeche","year":"2019","unstructured":"Yeche H, Harrison J, Berthier T (2019) UBS: a dimension-agnostic metric for concept vector interpretability applied to radiomics. Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support. Springer, Cham, pp 12\u201320"},{"key":"437_CR107","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-030-33850-3_6","volume-title":"Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support","author":"K Young","year":"2019","unstructured":"Young K, Booth G, Simpson B, Dutton R, Shrapnel S (2019) Deep neural network or dermatologist? Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support. Springer, Cham, pp 48\u201355"},{"key":"437_CR108","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2014) \u201cVisualizing and understanding convolutional networks.\u201d In Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part I 13, pp. 818\u2013833. Springer International Publishing","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"437_CR109","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1007\/s11548-022-02619-x","volume":"17","author":"RA Zeineldin","year":"2022","unstructured":"Zeineldin RA, Karar ME, Elshaer Z et al (2022a) Explainability of deep neural networks for MRI analysis of brain tumors. Int J CARS 17:1673\u20131683. https:\/\/doi.org\/10.1007\/s11548-022-02619-x","journal-title":"Int J CARS"},{"issue":"9","key":"437_CR110","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1007\/s11548-022-02619-x","volume":"17","author":"RA Zeineldin","year":"2022","unstructured":"Zeineldin RA, Karar ME, Elshaer Z, Coburger J, Wirtz CR, Burgert O, Mathis-Ullrich F (2022b) Explainability of deep neural networks for MRI analysis of brain tumors. Int J Comput Assisted Radiol Surg 17(9):1673\u20131683","journal-title":"Int J Comput Assisted Radiol Surg"},{"key":"437_CR111","doi-asserted-by":"crossref","unstructured":"Zhang Z, Xie Y, Xing F, McGough M, Yang L (2017) Mdnet: A semantically and visually interpretable medical image diagnosis network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21\u201326 July 2017; pp. 6428\u20136436","DOI":"10.1109\/CVPR.2017.378"},{"key":"437_CR112","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-030-33850-3_5","volume-title":"Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support","author":"P Zhu","year":"2019","unstructured":"Zhu P, Ogino M (2019) Guideline-based additive explanation for computer-aided diagnosis of lung nodules. Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support. Springer, Cham, pp 39\u201347"},{"key":"437_CR113","unstructured":"Zintgraf LM, Cohen TS, Adel T, Welling M (2017) \u201cVisualizing deep neural network decisions: Prediction difference analysis.\u201d arXiv preprint arXiv:1702.04595"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-023-00437-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-023-00437-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-023-00437-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T20:45:40Z","timestamp":1733949940000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-023-00437-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":113,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["437"],"URL":"https:\/\/doi.org\/10.1007\/s13721-023-00437-y","relation":{},"ISSN":["2192-6670"],"issn-type":[{"value":"2192-6670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,22]]},"assertion":[{"value":"25 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors have no conflict to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"4"}}