{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:45:14Z","timestamp":1777895114052,"version":"3.51.4"},"reference-count":72,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2024-00449891"],"award-info":[{"award-number":["RS-2024-00449891"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute of Information & Communications Technology Planning & Evaluation","doi-asserted-by":"publisher","award":["IITP-2025-RS-2024-00437718"],"award-info":[{"award-number":["IITP-2025-RS-2024-00437718"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2025-RISE-03-001"],"award-info":[{"award-number":["2025-RISE-03-001"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.bspc.2026.109766","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T13:03:22Z","timestamp":1771247002000},"page":"109766","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Refining explainability in chest X-ray diagnostics with lesion-aware hybrid transformer and local similarity of integrated re-normalized attention map"],"prefix":"10.1016","volume":"119","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7778-7265","authenticated-orcid":false,"given":"Ho Kyung","family":"Shin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6206-4745","authenticated-orcid":false,"given":"Deok Heon","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2070-3980","authenticated-orcid":false,"given":"Hanna","family":"Jung","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6548-4486","authenticated-orcid":false,"given":"Woo-Jeoung","family":"Nam","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.109766_b1","series-title":"2008 Report on Sources and Effects of Ionizing Radiation","author":"UnitedNations","year":"2008"},{"issue":"3","key":"10.1016\/j.bspc.2026.109766_b2","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.jacr.2016.09.039","article-title":"Utilization trends in noncardiac thoracic imaging, 2002\u20132014","volume":"14","author":"Kamel","year":"2017","journal-title":"J. Am. Coll. Radiol."},{"key":"10.1016\/j.bspc.2026.109766_b3","series-title":"Clinical Radiology: the Essentials","author":"Daffner","year":"2013"},{"issue":"2","key":"10.1016\/j.bspc.2026.109766_b4","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.232558","article-title":"Fleischner society: glossary of terms for thoracic imaging","volume":"310","author":"Bankier","year":"2024","journal-title":"Radiology"},{"issue":"2","key":"10.1016\/j.bspc.2026.109766_b5","article-title":"Diagnostic radiology in Liberia: a country report","volume":"1","author":"Ali","year":"2015","journal-title":"J. Glob. Radiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.109766_b6","article-title":"Diagnostic radiology in Nigeria: a country report","volume":"6","author":"Idowu","year":"2020","journal-title":"J. Glob. Radiol."},{"key":"10.1016\/j.bspc.2026.109766_b7","article-title":"Radiologist shortage leaves patient care at risk, warns royal college","volume":"359","author":"Rimmer","year":"2017","journal-title":"BMJ: Br. Med. J. (Online)"},{"key":"10.1016\/j.bspc.2026.109766_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102125","article-title":"Deep learning for chest X-ray analysis: A survey. Medical image analysis","volume":"72","author":"\u00c7all\u0131","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.109766_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102444","article-title":"Recent advances and clinical applications of deep learning in medical image analysis","volume":"79","author":"Chen","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.109766_b10","series-title":"Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning","author":"Rajpurkar","year":"2017"},{"key":"10.1016\/j.bspc.2026.109766_b11","doi-asserted-by":"crossref","unstructured":"X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R.M. Summers, Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2097\u20132106.","DOI":"10.1109\/CVPR.2017.369"},{"key":"10.1016\/j.bspc.2026.109766_b12","series-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings 23","first-page":"757","article-title":"Learning to recognize abnormalities in chest x-rays with location-aware dense networks","author":"Guendel","year":"2019"},{"key":"10.1016\/j.bspc.2026.109766_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2019.04.031","article-title":"DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays","volume":"53","author":"Chen","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"issue":"7","key":"10.1016\/j.bspc.2026.109766_b14","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1109\/JBHI.2019.2952597","article-title":"Lesion location attention guided network for multi-label thoracic disease classification in chest X-rays","volume":"24","author":"Chen","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.109766_b15","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compmedimag.2019.05.005","article-title":"SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images","volume":"75","author":"Liu","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.bspc.2026.109766_b16","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.patrec.2018.10.027","article-title":"Multi-label chest X-ray image classification via category-wise residual attention learning","volume":"130","author":"Guan","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.bspc.2026.109766_b17","doi-asserted-by":"crossref","first-page":"64279","DOI":"10.1109\/ACCESS.2019.2916849","article-title":"A novel approach for multi-label chest X-ray classification of common thorax diseases","volume":"7","author":"Allaouzi","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.109766_b18","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"730","article-title":"Multi-label thoracic disease image classification with cross-attention networks","author":"Ma","year":"2019"},{"key":"10.1016\/j.bspc.2026.109766_b19","series-title":"BIOCOMPUTING 2021: Proceedings of the Pacific Symposium","first-page":"232","article-title":"CheXclusion: Fairness gaps in deep chest X-ray classifiers","author":"Seyyed-Kalantari","year":"2020"},{"issue":"8","key":"10.1016\/j.bspc.2026.109766_b20","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.1109\/JBHI.2020.2967084","article-title":"Label co-occurrence learning with graph convolutional networks for multi-label chest x-ray image classification","volume":"24","author":"Chen","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"9","key":"10.1016\/j.bspc.2026.109766_b21","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s11517-022-02604-1","article-title":"Modeling global and local label correlation with graph convolutional networks for multi-label chest X-ray image classification","volume":"60","author":"Li","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"issue":"4","key":"10.1016\/j.bspc.2026.109766_b22","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1109\/TCSVT.2021.3079900","article-title":"Multi-label chest X-ray image classification via semantic similarity graph embedding","volume":"32","author":"Chen","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.bspc.2026.109766_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111575","article-title":"Tuberculosis and pneumonia diagnosis in chest X-rays by large adaptive filter and aligning normalized network with report-guided multi-level alignment","volume":"158","author":"Lu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.bspc.2026.109766_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106981","article-title":"CTBViT: A novel ViT for tuberculosis classification with efficient block and randomized classifier","volume":"100","author":"Lu","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.109766_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121141","article-title":"OPT-CO: Optimizing pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks","volume":"680","author":"Zhu","year":"2024","journal-title":"Inform. Sci."},{"key":"10.1016\/j.bspc.2026.109766_b26","series-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018"},{"key":"10.1016\/j.bspc.2026.109766_b27","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.bspc.2026.109766_b28","series-title":"On the opportunities and risks of foundation models","author":"Bommasani","year":"2021"},{"key":"10.1016\/j.bspc.2026.109766_b29","series-title":"Swinchex: Multi-label classification on chest x-ray images with transformers","author":"Taslimi","year":"2022"},{"key":"10.1016\/j.bspc.2026.109766_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107141","article-title":"IEViT: An enhanced vision transformer architecture for chest X-ray image classification","volume":"226","author":"Okolo","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.109766_b31","series-title":"2023 IEEE International Symposium on Medical Measurements and Applications","first-page":"1","article-title":"CheXViT: CheXNet and vision transformer to multi-label chest X-Ray image classification","author":"Faisal","year":"2023"},{"key":"10.1016\/j.bspc.2026.109766_b32","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"10.1016\/j.bspc.2026.109766_b33","series-title":"Quantifying attention flow in transformers","author":"Abnar","year":"2020"},{"key":"10.1016\/j.bspc.2026.109766_b34","doi-asserted-by":"crossref","unstructured":"H. Chefer, S. Gur, L. Wolf, Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 397\u2013406.","DOI":"10.1109\/ICCV48922.2021.00045"},{"key":"10.1016\/j.bspc.2026.109766_b35","doi-asserted-by":"crossref","unstructured":"S. Lapuschkin, A. Binder, G. Montavon, K.-R. Muller, W. Samek, Analyzing classifiers: Fisher vectors and deep neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2912\u20132920.","DOI":"10.1109\/CVPR.2016.318"},{"issue":"10","key":"10.1016\/j.bspc.2026.109766_b36","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1007\/s11263-017-1059-x","article-title":"Top-down neural attention by excitation backprop","volume":"126","author":"Zhang","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.bspc.2026.109766_b37","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.bspc.2026.109766_b38","series-title":"European Conference on Computer Vision","first-page":"213","article-title":"End-to-end object detection with transformers","author":"Carion","year":"2020"},{"key":"10.1016\/j.bspc.2026.109766_b39","doi-asserted-by":"crossref","unstructured":"W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, L. Shao, Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 568\u2013578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10.1016\/j.bspc.2026.109766_b40","doi-asserted-by":"crossref","unstructured":"Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.bspc.2026.109766_b41","series-title":"International Conference on Machine Learning","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2021"},{"key":"10.1016\/j.bspc.2026.109766_b42","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.bspc.2026.109766_b43","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1007\/s12559-022-10032-4","article-title":"MXT: A new variant of pyramid vision transformer for multi-label chest X-ray image classification","volume":"14","author":"Jiang","year":"2022","journal-title":"Cogn. Comput."},{"key":"10.1016\/j.bspc.2026.109766_b44","doi-asserted-by":"crossref","unstructured":"R. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer, High-resolution image synthesis with latent diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10.1016\/j.bspc.2026.109766_b45","series-title":"International Conference on Machine Learning","first-page":"8821","article-title":"Zero-shot text-to-image generation","author":"Ramesh","year":"2021"},{"key":"10.1016\/j.bspc.2026.109766_b46","series-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.bspc.2026.109766_b47","series-title":"International Conference on Machine Learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.bspc.2026.109766_b48","doi-asserted-by":"crossref","first-page":"23716","DOI":"10.52202\/068431-1723","article-title":"Flamingo: a visual language model for few-shot learning","volume":"35","author":"Alayrac","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.109766_b49","series-title":"Coca: Contrastive captioners are image-text foundation models","author":"Yu","year":"2022"},{"issue":"4","key":"10.1016\/j.bspc.2026.109766_b50","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"10.1016\/j.bspc.2026.109766_b51","first-page":"1","article-title":"Domain-specific language model pretraining for biomedical natural language processing","volume":"3","author":"Gu","year":"2021","journal-title":"ACM Trans. Comput. Heal. (HEALTH)"},{"issue":"6","key":"10.1016\/j.bspc.2026.109766_b52","doi-asserted-by":"crossref","first-page":"bbac409","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.bspc.2026.109766_b53","series-title":"Towards generalist biomedical ai","author":"Tu","year":"2023"},{"key":"10.1016\/j.bspc.2026.109766_b54","series-title":"Machine Learning for Health","first-page":"353","article-title":"Med-flamingo: a multimodal medical few-shot learner","author":"Moor","year":"2023"},{"key":"10.1016\/j.bspc.2026.109766_b55","article-title":"Llava-med: Training a large language-and-vision assistant for biomedicine in one day","volume":"36","author":"Li","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.109766_b56","series-title":"Smoothgrad: removing noise by adding noise","author":"Smilkov","year":"2017"},{"key":"10.1016\/j.bspc.2026.109766_b57","series-title":"International Conference on Machine Learning","first-page":"3319","article-title":"Axiomatic attribution for deep networks","author":"Sundararajan","year":"2017"},{"key":"10.1016\/j.bspc.2026.109766_b58","series-title":"Rise: Randomized input sampling for explanation of black-box models","author":"Petsiuk","year":"2018"},{"key":"10.1016\/j.bspc.2026.109766_b59","doi-asserted-by":"crossref","unstructured":"R. Fong, M. Patrick, A. Vedaldi, Understanding deep networks via extremal perturbations and smooth masks, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 2950\u20132958.","DOI":"10.1109\/ICCV.2019.00304"},{"key":"10.1016\/j.bspc.2026.109766_b60","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","article-title":"Explaining nonlinear classification decisions with deep taylor decomposition","volume":"65","author":"Montavon","year":"2017","journal-title":"Pattern Recognit."},{"issue":"7","key":"10.1016\/j.bspc.2026.109766_b61","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0130140","article-title":"On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation","volume":"10","author":"Bach","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2026.109766_b62","doi-asserted-by":"crossref","unstructured":"W.-J. Nam, S. Gur, J. Choi, L. Wolf, S.-W. Lee, Relative attributing propagation: Interpreting the comparative contributions of individual units in deep neural networks, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 2501\u20132508, 03.","DOI":"10.1609\/aaai.v34i03.5632"},{"key":"10.1016\/j.bspc.2026.109766_b63","doi-asserted-by":"crossref","unstructured":"H. Chefer, S. Gur, L. Wolf, Transformer interpretability beyond attention visualization, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 782\u2013791.","DOI":"10.1109\/CVPR46437.2021.00084"},{"key":"10.1016\/j.bspc.2026.109766_b64","doi-asserted-by":"crossref","unstructured":"M. Caron, P. Bojanowski, J. Mairal, A. Joulin, Unsupervised pre-training of image features on non-curated data, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 2959\u20132968.","DOI":"10.1109\/ICCV.2019.00305"},{"key":"10.1016\/j.bspc.2026.109766_b65","series-title":"Dinov2: Learning robust visual features without supervision","author":"Oquab","year":"2023"},{"key":"10.1016\/j.bspc.2026.109766_b66","series-title":"ibot: Image bert pre-training with online tokenizer","author":"Zhou","year":"2021"},{"key":"10.1016\/j.bspc.2026.109766_b67","series-title":"Spreading vectors for similarity search","author":"Sablayrolles","year":"2018"},{"key":"10.1016\/j.bspc.2026.109766_b68","article-title":"Fixing the train-test resolution discrepancy","volume":"32","author":"Touvron","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.109766_b69","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.109766_b70","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"101","article-title":"Cxr-clip: Toward large scale chest x-ray language-image pre-training","author":"You","year":"2023"},{"key":"10.1016\/j.bspc.2026.109766_b71","series-title":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference","first-page":"0280","article-title":"Thin mobilenet: An enhanced mobilenet architecture","author":"Sinha","year":"2019"},{"key":"10.1016\/j.bspc.2026.109766_b72","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426003204?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426003204?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T07:42:42Z","timestamp":1777621362000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426003204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":72,"alternative-id":["S1746809426003204"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109766","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Refining explainability in chest X-ray diagnostics with lesion-aware hybrid transformer and local similarity of integrated re-normalized attention map","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109766","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109766"}}