{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T03:33:24Z","timestamp":1777865604462,"version":"3.51.4"},"reference-count":59,"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\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202208420109"],"award-info":[{"award-number":["202208420109"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["2022013988065212"],"award-info":[{"award-number":["2022013988065212"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018570","name":"Special Project of Central Government for Local Science and Technology Development of Hubei Province","doi-asserted-by":"publisher","award":["2024EIA003"],"award-info":[{"award-number":["2024EIA003"]}],"id":[{"id":"10.13039\/501100018570","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202346"],"award-info":[{"award-number":["62202346"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012140","name":"Wuhan Textile University","doi-asserted-by":"publisher","award":["2024442"],"award-info":[{"award-number":["2024442"]}],"id":[{"id":"10.13039\/501100012140","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Image and Vision Computing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.imavis.2026.105972","type":"journal-article","created":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:12:35Z","timestamp":1775315555000},"page":"105972","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DFFENet: Dual-Branch Frequency Domain Feature Enhancement Network for Skin Lesion Classification"],"prefix":"10.1016","volume":"170","author":[{"given":"Feng","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyu","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6421-8613","authenticated-orcid":false,"given":"Minghua","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.imavis.2026.105972_b1","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA: Cancer J. Clin."},{"key":"10.1016\/j.imavis.2026.105972_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102305","article-title":"Analysis of the ISIC image datasets: Usage, benchmarks and recommendations","volume":"75","author":"Cassidy","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.imavis.2026.105972_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126223","article-title":"BiaCanDet: Bioelectrical impedance analysis for breast cancer detection with space\u2013time attention neural network","volume":"269","author":"Yu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.imavis.2026.105972_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105166","article-title":"PMANet: Progressive multi-stage attention networks for skin disease classification","volume":"149","author":"Zhao","year":"2024","journal-title":"Image Vis. Comput."},{"issue":"2","key":"10.1016\/j.imavis.2026.105972_b5","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s11831-023-10005-2","article-title":"An analysis of detection and diagnosis of different classes of skin diseases using artificial intelligence-based learning approaches with hyper parameters","volume":"31","author":"Singh","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.imavis.2026.105972_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104779","article-title":"OESV-KRF: Optimal ensemble support vector kernel random forest based early detection and classification of skin diseases","volume":"85","author":"Kalpana","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.imavis.2026.105972_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102631","article-title":"Computer-aided skin cancer diagnosis based on a new meta-heuristic algorithm combined with support vector method","volume":"68","author":"Bi","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.imavis.2026.105972_b8","article-title":"Enhancing skin cancer classification with Soft Attention and genetic algorithm-optimized ensemble learning","author":"Ranjan","year":"2025","journal-title":"Image Vis. Comput."},{"issue":"7","key":"10.1016\/j.imavis.2026.105972_b9","doi-asserted-by":"crossref","first-page":"6377","DOI":"10.1109\/JIOT.2022.3224947","article-title":"Smart clothing system with multiple sensors based on digital twin technology","volume":"10","author":"Yu","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.imavis.2026.105972_b10","article-title":"MedSetFeat++: An attention-enriched set feature framework for few-shot medical image classification","author":"Titoriya","year":"2025","journal-title":"Image Vis. Comput."},{"issue":"4","key":"10.1016\/j.imavis.2026.105972_b11","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/TCDS.2024.3375620","article-title":"Converting artificial neural networks to ultra-low-latency spiking neural networks for action recognition","volume":"16","author":"You","year":"2024","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"10.1016\/j.imavis.2026.105972_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2025.105440","article-title":"MAFMv3: An automated Multi-Scale Attention-Based Feature Fusion MobileNetv3 for spine lesion classification","volume":"155","author":"Dastgir","year":"2025","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.105972_b13","article-title":"A novel end-to-end deep convolutional neural network based skin lesion classification framework","volume":"246","author":"Sulthana","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.imavis.2026.105972_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.104969","article-title":"An improved skin lesion detection solution using multi-step preprocessing features and NASNet transfer learning model","volume":"144","author":"Altamimi","year":"2024","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.105972_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107413","article-title":"Explainable skin lesion diagnosis using taxonomies","volume":"110","author":"Barata","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.imavis.2026.105972_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108369","article-title":"Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition","volume":"173","author":"He","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.imavis.2026.105972_b17","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TMM.2022.3152367","article-title":"VTON-SCFA: A virtual try-on network based on the semantic constraints and flow alignment","volume":"25","author":"Du","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.imavis.2026.105972_b18","series-title":"European Conference on Computer Vision","first-page":"467","article-title":"Trojvlm: Backdoor attack against vision language models","author":"Lyu","year":"2024"},{"key":"10.1016\/j.imavis.2026.105972_b19","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TMM.2022.3141886","article-title":"Graph complemented latent representation for few-shot image classification","volume":"25","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.imavis.2026.105972_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111794","article-title":"A lightweight deep convolutional neural network model for skin cancer image classification","author":"Tuncer","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.imavis.2026.105972_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110742","article-title":"Self-contrastive Feature Guidance Based Multidimensional Collaborative Network of metadata and image features for skin disease classification","volume":"156","author":"Li","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.imavis.2026.105972_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.104065","article-title":"Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities","volume":"127","author":"Goyal","year":"2020","journal-title":"Comput. Biol. Med."},{"issue":"15","key":"10.1016\/j.imavis.2026.105972_b23","doi-asserted-by":"crossref","first-page":"26314","DOI":"10.1109\/JIOT.2024.3394244","article-title":"Intelligent wearable system with motion and emotion recognition based on digital twin technology","volume":"11","author":"Yu","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.imavis.2026.105972_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111182","article-title":"DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection","volume":"159","author":"Reis","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.imavis.2026.105972_b25","doi-asserted-by":"crossref","unstructured":"K. Jiang, W. Liu, Z. Wang, X. Zhong, J. Jiang, C.-W. Lin, Dawn: Direction-aware attention wavelet network for image deraining, in: Proceedings of the 31st ACM International Conference on Multimedia, 2023, pp. 7065\u20137074.","DOI":"10.1145\/3581783.3611697"},{"issue":"9","key":"10.1016\/j.imavis.2026.105972_b26","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","article-title":"Attention residual learning for skin lesion classification","volume":"38","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.imavis.2026.105972_b27","doi-asserted-by":"crossref","first-page":"2482","DOI":"10.1109\/TMI.2020.2972964","article-title":"A mutual bootstrapping model for automated skin lesion segmentation and classification","volume":"39","author":"Xie","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.imavis.2026.105972_b28","doi-asserted-by":"crossref","unstructured":"F. Yu, J. Cao, L. Liu, M. Jiang, SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor Segmentation, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 5197\u20135206.","DOI":"10.1109\/CVPR52734.2025.00490"},{"key":"10.1016\/j.imavis.2026.105972_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102307","article-title":"FusionM4Net: A multi-stage multi-modal learning algorithm for multi-label skin lesion classification","volume":"76","author":"Tang","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.imavis.2026.105972_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111624","article-title":"Explainable deep inherent learning for multi-classes skin lesion classification","volume":"159","author":"Hosny","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.imavis.2026.105972_b31","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neunet.2023.01.022","article-title":"Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare","volume":"160","author":"Maqsood","year":"2023","journal-title":"Neural Netw."},{"key":"10.1016\/j.imavis.2026.105972_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102693","article-title":"Ssd-kd: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images","volume":"84","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.imavis.2026.105972_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119230","article-title":"Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images","volume":"213","author":"Anand","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.imavis.2026.105972_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105757","article-title":"DCENSnet: A new deep convolutional ensemble network for skin cancer classification","volume":"89","author":"Chanda","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.imavis.2026.105972_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102746","article-title":"Intra-class consistency and inter-class discrimination feature learning for automatic skin lesion classification","volume":"85","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.imavis.2026.105972_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113922","article-title":"Data augmentation for skin lesion using self-attention based progressive generative adversarial network","volume":"165","author":"Abdelhalim","year":"2021","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"10.1016\/j.imavis.2026.105972_b37","doi-asserted-by":"crossref","first-page":"17634","DOI":"10.1109\/JIOT.2025.3538601","article-title":"Multimodal wearable system with dual-frequency enhancement network for risk recognition","volume":"12","author":"Yu","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.imavis.2026.105972_b38","series-title":"International Conference on Medical Imaging and Informatics","first-page":"99","article-title":"Learning a frequency\u2013based weighting for medical image classification","author":"Gass","year":"2007"},{"key":"10.1016\/j.imavis.2026.105972_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119064","article-title":"Wavelet transform based deep residual neural network and ReLU based Extreme Learning Machine for skin lesion classification","volume":"213","author":"Alenezi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.imavis.2026.105972_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103423","article-title":"Gabor wavelet-based deep learning for skin lesion classification","volume":"113","author":"Serte","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.imavis.2026.105972_b41","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.compmedimag.2018.05.004","article-title":"Automatic histologically-closer classification of skin lesions","volume":"68","author":"Rebou\u00e7as Filho","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.imavis.2026.105972_b42","series-title":"International Conference on Machine Learning","first-page":"10096","article-title":"Efficientnetv2: Smaller models and faster training","author":"Tan","year":"2021"},{"key":"10.1016\/j.imavis.2026.105972_b43","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.imavis.2026.105972_b44","series-title":"Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC) [dataset]","author":"Gutman","year":"2016"},{"key":"10.1016\/j.imavis.2026.105972_b45","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging","first-page":"168","article-title":"Skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC) [dataset]","author":"Codella","year":"2018"},{"key":"10.1016\/j.imavis.2026.105972_b46","series-title":"2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"5437","article-title":"PH 2-A dermoscopic image database for research and benchmarking [dataset]","author":"Mendon\u00e7a","year":"2013"},{"key":"10.1016\/j.imavis.2026.105972_b47","series-title":"International Conference on Machine Learning","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.imavis.2026.105972_b48","doi-asserted-by":"crossref","unstructured":"I. Radosavovic, R.P. Kosaraju, R. Girshick, K. He, P. Doll\u00e1r, Designing network design spaces, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 10428\u201310436.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"10.1016\/j.imavis.2026.105972_b49","doi-asserted-by":"crossref","unstructured":"Q. Li, L. Shen, S. Guo, Z. Lai, Wavelet integrated CNNs for noise-robust image classification, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 7245\u20137254.","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"10.1016\/j.imavis.2026.105972_b50","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.imavis.2026.105972_b51","doi-asserted-by":"crossref","unstructured":"Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, A convnet for the 2020s, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11976\u201311986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10.1016\/j.imavis.2026.105972_b52","doi-asserted-by":"crossref","unstructured":"J. Chen, S.-h. Kao, H. He, W. Zhuo, S. Wen, C.-H. Lee, S.-H.G. Chan, Run, don\u2019t walk: chasing higher FLOPS for faster neural networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 12021\u201312031.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"10.1016\/j.imavis.2026.105972_b53","doi-asserted-by":"crossref","unstructured":"W. Wang, J. Dai, Z. Chen, Z. Huang, Z. Li, X. Zhu, X. Hu, T. Lu, L. Lu, H. Li, et al., Internimage: Exploring large-scale vision foundation models with deformable convolutions, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14408\u201314419.","DOI":"10.1109\/CVPR52729.2023.01385"},{"key":"10.1016\/j.imavis.2026.105972_b54","doi-asserted-by":"crossref","unstructured":"S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I.S. Kweon, S. Xie, Convnext v2: Co-designing and scaling convnets with masked autoencoders, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 16133\u201316142.","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"10.1016\/j.imavis.2026.105972_b55","series-title":"European Conference on Computer Vision","first-page":"78","article-title":"MobileNetV4: universal models for the mobile ecosystem","author":"Qin","year":"2024"},{"key":"10.1016\/j.imavis.2026.105972_b56","series-title":"European Conference on Computer Vision","first-page":"395","article-title":"DenseNets reloaded: paradigm shift beyond ResNets and vits","author":"Kim","year":"2024"},{"key":"10.1016\/j.imavis.2026.105972_b57","article-title":"Ensemble cross UNet transformers for augmentation of atomic electron tomography","volume":"73","author":"Yu","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.imavis.2026.105972_b58","doi-asserted-by":"crossref","unstructured":"W. Lyu, S. Zheng, T. Ma, C. Chen, A Study of the Attention Abnormality in Trojaned BERTs, in: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022, pp. 4727\u20134741.","DOI":"10.18653\/v1\/2022.naacl-main.348"},{"key":"10.1016\/j.imavis.2026.105972_b59","doi-asserted-by":"crossref","unstructured":"B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921\u20132929.","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Image and Vision Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S026288562600079X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S026288562600079X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T07:00:33Z","timestamp":1777532433000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S026288562600079X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":59,"alternative-id":["S026288562600079X"],"URL":"https:\/\/doi.org\/10.1016\/j.imavis.2026.105972","relation":{},"ISSN":["0262-8856"],"issn-type":[{"value":"0262-8856","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DFFENet: Dual-Branch Frequency Domain Feature Enhancement Network for Skin Lesion Classification","name":"articletitle","label":"Article Title"},{"value":"Image and Vision Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.imavis.2026.105972","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"105972"}}