{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:13:30Z","timestamp":1783700010085,"version":"3.55.0"},"reference-count":72,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100016017","name":"Duke-NUS Medical School","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016017","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.jbi.2024.104673","type":"journal-article","created":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T09:59:08Z","timestamp":1717927148000},"page":"104673","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification"],"prefix":"10.1016","volume":"156","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2674-6068","authenticated-orcid":false,"given":"Han","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng-Tao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gangming","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nguyen Tuan Anh","family":"Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yet Yen","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3610-4883","authenticated-orcid":false,"given":"Nan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.jbi.2024.104673_b0005","doi-asserted-by":"crossref","DOI":"10.1001\/jama.2017.10476","article-title":"Pneumothorax","volume":"318","author":"Imran","year":"2017","journal-title":"J. Am. Med. Assoc."},{"issue":"4","key":"10.1016\/j.jbi.2024.104673_b0010","doi-asserted-by":"crossref","first-page":"e200190","DOI":"10.1148\/ryai.2021200190","article-title":"Tiong VTY: deep learning systems for pneumothorax detection on chest radiographs: a multicenter external validation study","volume":"3","author":"Thian","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.jbi.2024.104673_b0015","first-page":"6105","article-title":"Efficientnet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019","journal-title":"Proc. Int. Conf. Mach. Learn."},{"key":"10.1016\/j.jbi.2024.104673_b0020","first-page":"1251","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"3","key":"10.1016\/j.jbi.2024.104673_b0025","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/TCBB.2019.2911947","article-title":"Enhanced diagnosis of pneumothorax with an improved real-time augmentation for imbalanced chest X-rays data based on DCNN","volume":"18","author":"Wang","year":"2019","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0030","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TTS.2023.3234203","article-title":"Challenges of deep learning in medical image analysis\u2014improving explainability and trust","volume":"4","author":"Dhar","year":"2023","journal-title":"IEEE Trans. Technol. Soc."},{"key":"10.1016\/j.jbi.2024.104673_b0035","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2022.10.019","article-title":"Weakly supervised thoracic disease localization via disease masks","volume":"517","author":"Jung","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jbi.2024.104673_b0040","article-title":"Handling missing values in healthcare data: a systematic review of deep learning-based imputation techniques","volume":"102587","author":"Liu","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.jbi.2024.104673_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106130","article-title":"Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures","volume":"206","author":"Mosquera","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.jbi.2024.104673_b0050","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"Zhou","year":"2016","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"8","key":"10.1016\/j.jbi.2024.104673_b0055","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1111\/1754-9485.13393","article-title":"Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms","volume":"66","author":"Feng","year":"2022","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"10.1016\/j.jbi.2024.104673_b0060","first-page":"618","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"10.1016\/j.jbi.2024.104673_b0065","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"10","key":"10.1016\/j.jbi.2024.104673_b0070","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1038\/s42256-022-00536-x","article-title":"Benchmarking saliency methods for chest X-ray interpretation","volume":"4","author":"Saporta","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.jbi.2024.104673_b0075","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"10.1016\/j.jbi.2024.104673_b0080","first-page":"4116","article-title":"Confident-CAM: improving heat map interpretation in chest X-ray image classification","author":"Rocha","year":"2023","journal-title":"Proc. Int. Conf. Bioinformatics Biomed."},{"key":"10.1016\/j.jbi.2024.104673_b0085","series-title":"Fairness and Explainability in Chest X-ray Image Classifiers","author":"Bel Bordes","year":"2023"},{"key":"10.1016\/j.jbi.2024.104673_b0090","article-title":"Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets","author":"Crosby","year":"2020","journal-title":"Proc. SPIE Med. Imag."},{"key":"10.1016\/j.jbi.2024.104673_b0095","article-title":"Deep inside convolutional networks: visualising image classification models and saliency maps","author":"Simonyan","year":"2013","journal-title":"Proc. Int. Conf. Learn. Represent."},{"key":"10.1016\/j.jbi.2024.104673_b0100","first-page":"3319","article-title":"Axiomatic attribution for deep networks","author":"Sundararajan","year":"2017","journal-title":"Proc. Int. Conf. Mach. Learn."},{"issue":"4","key":"10.1016\/j.jbi.2024.104673_b0105","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1007\/s10462-022-10246-w","article-title":"Human-in-the-loop machine learning: a state of the art","volume":"56","author":"Mosqueira-Rey","year":"2023","journal-title":"Artif. Intell. Rev."},{"issue":"9","key":"10.1016\/j.jbi.2024.104673_b0110","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: a comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0115","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s41591-018-0320-3","article-title":"A call for deep-learning healthcare","volume":"25","author":"Norgeot","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.jbi.2024.104673_b0120","article-title":"Deep learning for temporal data representation in electronic health records: a systematic review of challenges and methodologies","volume":"103980","author":"Xie","year":"2021","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2024.104673_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106668","article-title":"Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks","author":"Nazir","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.jbi.2024.104673_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"Van der Velden","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.jbi.2024.104673_b0135","article-title":"Sanity checks for saliency maps","author":"Adebayo","year":"2018","journal-title":"Proc. Adv. Neural Inf. Process. Syst.: 2018"},{"issue":"2","key":"10.1016\/j.jbi.2024.104673_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.103225","article-title":"Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations","volume":"60","author":"Amorim","year":"2023","journal-title":"Inf. Process. Manag."},{"issue":"3","key":"10.1016\/j.jbi.2024.104673_b0145","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.4269\/ajtmh.20-0815","article-title":"Case report: pneumothorax and pneumomediastinum as uncommon complications of COVID-19 pneumonia\u2014literature review","volume":"103","author":"Quincho-Lopez","year":"2020","journal-title":"Am. J. Trop. Med. Hyg."},{"key":"10.1016\/j.jbi.2024.104673_b0150","first-page":"10632","article-title":"Align, attend and locate: chest x-ray diagnosis via contrast induced attention network with limited supervision","author":"Liu","year":"2019","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"10.1016\/j.jbi.2024.104673_b0155","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.cmpb.2019.06.005","article-title":"An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks","volume":"177","author":"Souza","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.jbi.2024.104673_b0160","unstructured":"Template guidance code [https:\/\/github.com\/Han-Yuan-Med\/template-explanation]."},{"key":"10.1016\/j.jbi.2024.104673_b0165","unstructured":"SIIM-ACR Pneumothorax Segmentation Challenge [https:\/\/kaggle.com\/competitions\/siim-acr-pneumothorax-segmentation]."},{"key":"10.1016\/j.jbi.2024.104673_b0170","unstructured":"J. Liu, J. Lian, Y. Yu, Chestx-det10: chest x-ray dataset on detection of thoracic abnormalities, arXiv 2020."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0175","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TCDS.2020.3035572","article-title":"Automatic segmentation of pneumothorax in chest radiographs based on a two-stage deep learning method","volume":"14","author":"Wang","year":"2020","journal-title":"IEEE Trans. Cogn. Develop. Syst."},{"key":"10.1016\/j.jbi.2024.104673_b0180","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.neucom.2021.05.029","article-title":"DeepSDM: boundary-aware pneumothorax segmentation in chest X-ray images","volume":"454","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jbi.2024.104673_b0185","article-title":"automated pneumothorax segmentation and quantification algorithm based on deep learning","volume":"22","author":"Sae-Lim","year":"2024","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.jbi.2024.104673_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103086","article-title":"Self-supervised learning for medical image analysis: discriminative, restorative, or adversarial?","volume":"94","author":"Haghighi","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.jbi.2024.104673_b0195","first-page":"2757","article-title":"Chest X-ray feature pyramid sum model with diseased area data augmentation method","author":"Kim","year":"2023","journal-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.: 2023"},{"issue":"8","key":"10.1016\/j.jbi.2024.104673_b0200","doi-asserted-by":"crossref","first-page":"2042","DOI":"10.1109\/TMI.2021.3070847","article-title":"A structure-aware relation network for thoracic diseases detection and segmentation","volume":"40","author":"Lian","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"01","key":"10.1016\/j.jbi.2024.104673_b0205","first-page":"590","article-title":"Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison","volume":"33","author":"Irvin","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0210","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1038\/s41597-022-01498-w","article-title":"VinDr-CXR: an open dataset of chest X-rays with radiologist\u2019s annotations","volume":"9","author":"Nguyen","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.jbi.2024.104673_b0215","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015","journal-title":"Proc. Int. Conf. Learn. Represent."},{"key":"10.1016\/j.jbi.2024.104673_b0220","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neunet.2022.01.017","article-title":"The emergence of a concept in shallow neural networks","volume":"148","author":"Agliari","year":"2022","journal-title":"Neural Netw."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0225","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/JSAIT.2020.2991332","article-title":"Toward moderate overparameterization: global convergence guarantees for training shallow neural networks","volume":"1","author":"Oymak","year":"2020","journal-title":"IEEE J. Select. Areas Inf. Theory"},{"issue":"5","key":"10.1016\/j.jbi.2024.104673_b0230","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/JBHI.2019.2949075","article-title":"Clinical interpretable deep learning model for glaucoma diagnosis","volume":"24","author":"Liao","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.jbi.2024.104673_b0235","first-page":"224","article-title":"Is visual explanation with Grad-CAM more reliable for deeper neural networks? a case study with automatic pneumothorax diagnosis","author":"Qiu","year":"2023","journal-title":"Proc. Int. Workshop Mach. Learn. Med. Imag."},{"key":"10.1016\/j.jbi.2024.104673_b0240","first-page":"17","article-title":"Prediction of adverse drug reaction using machine learning and deep learning based on an imbalanced electronic medical records dataset","author":"Zhao","year":"2021","journal-title":"Proc. Int. Conf. Med. Health Inform."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0245","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0250","first-page":"54","article-title":"Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy","volume":"1","author":"Efron","year":"1986","journal-title":"Stat. Sci."},{"key":"10.1016\/j.jbi.2024.104673_b0255","first-page":"11676","article-title":"C-cam: Causal cam for weakly supervised semantic segmentation on medical image","author":"Chen","year":"2022","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0260","doi-asserted-by":"crossref","first-page":"4314","DOI":"10.1038\/s41467-023-39902-7","article-title":"Detecting shortcut learning for fair medical AI using shortcut testing","volume":"14","author":"Brown","year":"2023","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0265","doi-asserted-by":"crossref","first-page":"6247","DOI":"10.1038\/s41598-023-32611-7","article-title":"A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays","volume":"13","author":"Innat","year":"2023","journal-title":"Sci. Rep."},{"issue":"9","key":"10.1016\/j.jbi.2024.104673_b0270","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.3390\/jcm10092016","article-title":"Radiological cardiothoracic ratio in evidence-based medicine","volume":"10","author":"Truszkiewicz","year":"2021","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.jbi.2024.104673_b0275","first-page":"498","article-title":"Improved deep learning explanations for prostate lesion classification through grad-CAM and saliency map fusion","author":"Gulum","year":"2021","journal-title":"Proc. IEEE Int. Symp. Comput.-Based Med. Syst."},{"key":"10.1016\/j.jbi.2024.104673_b0280","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Proc. Adv. Neural Inf. Proc. Syst."},{"key":"10.1016\/j.jbi.2024.104673_b0285","first-page":"839","article-title":"Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks","author":"Chattopadhay","year":"2018","journal-title":"Proc. IEEE Winter Conf. Appl. Comput. Vis."},{"issue":"2","key":"10.1016\/j.jbi.2024.104673_b0290","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/TAI.2022.3153754","article-title":"Ensemble image explainable AI (XAI) algorithm for severe community-acquired pneumonia and COVID-19 respiratory infections","volume":"4","author":"Zou","year":"2022","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0295","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-018-0065-x","article-title":"Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease","volume":"1","author":"Madani","year":"2018","journal-title":"npj Digital Med."},{"issue":"12","key":"10.1016\/j.jbi.2024.104673_b0300","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41551-022-00936-9","article-title":"Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning","volume":"6","author":"Tiu","year":"2022","journal-title":"Nat. Biomed. Eng."},{"issue":"11","key":"10.1016\/j.jbi.2024.104673_b0305","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1093\/jamia\/ocz089","article-title":"CAESNet: convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images","volume":"26","author":"Tong","year":"2019","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2024.104673_b0310","article-title":"Weakly supervised segmentation framework with uncertainty: a study on pneumothorax segmentation in chest X-ray","author":"Ouyang","year":"2019","journal-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Interv."},{"key":"10.1016\/j.jbi.2024.104673_b0315","article-title":"Constrained convolutional neural networks for weakly supervised segmentation","author":"Pathak","year":"2015","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"10.1016\/j.jbi.2024.104673_b0320","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","article-title":"Constrained-CNN losses for weakly supervised segmentation","volume":"54","author":"Kervadec","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.jbi.2024.104673_b0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: an experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"issue":"11","key":"10.1016\/j.jbi.2024.104673_b0330","first-page":"11945","article-title":"Evaluating explainable AI on a multi-modal medical imaging task: can existing algorithms fulfill clinical requirements?","volume":"36","author":"Jin","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0335","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-022-00658-x","article-title":"Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation","volume":"5","author":"Lee","year":"2022","journal-title":"npj Digital Med."},{"key":"10.1016\/j.jbi.2024.104673_b0340","article-title":"Human-guided design to explain deep learning-based pneumothorax classifier","author":"Yuan","year":"2023","journal-title":"Proc. Med. Imag. Deep Learn."},{"key":"10.1016\/j.jbi.2024.104673_b0345","doi-asserted-by":"crossref","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","article-title":"Layercam: exploring hierarchical class activation maps for localization","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.jbi.2024.104673_b0350","article-title":"Transformers in medical imaging: a survey","volume":"102802","author":"Shamshad","year":"2023","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.jbi.2024.104673_b0355","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-021-00416-5","article-title":"Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer","volume":"4","author":"Zeleznik","year":"2021","journal-title":"npj Digital Med."},{"issue":"8","key":"10.1016\/j.jbi.2024.104673_b0360","doi-asserted-by":"crossref","first-page":"e496","DOI":"10.1016\/S2589-7500(21)00106-0","article-title":"Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study","volume":"3","author":"Seah","year":"2021","journal-title":"Lancet Digital Health"}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046424000911?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046424000911?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T07:08:26Z","timestamp":1754032106000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046424000911"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":72,"alternative-id":["S1532046424000911"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2024.104673","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2024.104673","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"104673"}}