{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:08:09Z","timestamp":1780931289930,"version":"3.54.1"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009614","name":"Petroleum Technology Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009614","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.113988","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T23:29:49Z","timestamp":1779492589000},"page":"113988","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Frequency aided attention mechanism for better segmentation generalization and explainability"],"prefix":"10.1016","volume":"180","author":[{"given":"Mohammed","family":"Lawal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1702-9136","authenticated-orcid":false,"given":"Dewei","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.113988_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105194","article-title":"FRBNet: Feedback refinement boundary network for semantic segmentation in breast ultrasound images","volume":"86","author":"Li","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.patcog.2026.113988_b2","series-title":"2019 International Seminar on Intelligent Technology and Its Applications","first-page":"416","article-title":"Instance-aware semantic segmentation for food calorie estimation using mask R-CNN","author":"Yogaswara","year":"2019"},{"key":"10.1016\/j.patcog.2026.113988_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.101960","article-title":"Recent advances in medical image processing for the evaluation of chronic kidney disease","volume":"69","author":"Alnazer","year":"2021","journal-title":"Med. Image Anal."},{"issue":"12","key":"10.1016\/j.patcog.2026.113988_b4","doi-asserted-by":"crossref","first-page":"22694","DOI":"10.1109\/TITS.2022.3207665","article-title":"Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks","volume":"23","author":"Muhammad","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.patcog.2026.113988_b5","series-title":"2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin)","first-page":"1","article-title":"Semantic food segmentation for automatic dietary monitoring","author":"Aslan","year":"2018"},{"key":"10.1016\/j.patcog.2026.113988_b6","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113988_b7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.inffus.2021.05.005","article-title":"A defense method based on attention mechanism against traffic sign adversarial samples","volume":"76","author":"Li","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.113988_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.cobme.2026.100649","article-title":"Trustworthy AI in medical image analysis: A unified perspective built on robustness and layers of trust","author":"Zuluaga","year":"2026","journal-title":"Curr. Opin. Biomed. Eng."},{"key":"10.1016\/j.patcog.2026.113988_b9","unstructured":"A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, in: International Conference on Learning Representations, 2021."},{"key":"10.1016\/j.patcog.2026.113988_b10","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.patcog.2026.113988_b11","series-title":"2025 International Conference on Computing, Intelligence, and Application","first-page":"1","article-title":"Leveraging swin transformer for robust object detection in autonomous vehicles","author":"Sharma","year":"2025"},{"issue":"1","key":"10.1016\/j.patcog.2026.113988_b12","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/TBC.2018.2871376","article-title":"Full-reference image quality assessment by combining features in spatial and frequency domains","volume":"65","author":"Tang","year":"2018","journal-title":"IEEE Trans. Broadcast."},{"key":"10.1016\/j.patcog.2026.113988_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2024.104104","article-title":"MFCT: Multi-frequency cascade transformers for no-reference SR-IQA","volume":"248","author":"Fan","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.patcog.2026.113988_b14","doi-asserted-by":"crossref","first-page":"4764","DOI":"10.1109\/TMM.2023.3326300","article-title":"Ddaug: Differentiable data augmentation for weakly supervised semantic segmentation","volume":"26","author":"Li","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2026.113988_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2022.100258","article-title":"Data augmentation: A comprehensive survey of modern approaches","volume":"16","author":"Mumuni","year":"2022","journal-title":"Array"},{"issue":"24","key":"10.1016\/j.patcog.2026.113988_b16","doi-asserted-by":"crossref","first-page":"26329","DOI":"10.1007\/s11042-016-4128-1","article-title":"Machine learning-based framework for saliency detection in distorted images","volume":"76","author":"Niu","year":"2017","journal-title":"Multimedia Tools Appl."},{"issue":"2","key":"10.1016\/j.patcog.2026.113988_b17","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s11263-020-01383-2","article-title":"Benchmarking the robustness of semantic segmentation models with respect to common corruptions","volume":"129","author":"Kamann","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2026.113988_b18","series-title":"2025 International Joint Conference on Neural Networks","first-page":"1","article-title":"Robust Few-Shot semantic segmentation for blurred and occluded objects in construction environments","author":"Salimi","year":"2025"},{"issue":"2","key":"10.1016\/j.patcog.2026.113988_b19","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2026.113988_b20","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.patcog.2026.113988_b21","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.patcog.2026.113988_b22","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"481","article-title":"Ege-unet: an efficient group enhanced unet for skin lesion segmentation","author":"Ruan","year":"2023"},{"key":"10.1016\/j.patcog.2026.113988_b23","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"23","article-title":"Unext: Mlp-based rapid medical image segmentation network","author":"Valanarasu","year":"2022"},{"key":"10.1016\/j.patcog.2026.113988_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109298","article-title":"A multi-strategy contrastive learning framework for weakly supervised semantic segmentation","volume":"137","author":"Yuan","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113988_b25","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"},{"key":"10.1016\/j.patcog.2026.113988_b26","doi-asserted-by":"crossref","unstructured":"A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.-Y. Lo, et al., Segment anything, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.patcog.2026.113988_b27","first-page":"12934","article-title":"Efficientformer: Vision transformers at mobilenet speed","volume":"35","author":"Li","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113988_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108858","article-title":"SwinMas: Shifted windows and mask unit attention with auxiliary supervision for medical image segmentation","volume":"113","author":"Lawal","year":"2026","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.patcog.2026.113988_b29","doi-asserted-by":"crossref","first-page":"4757","DOI":"10.1109\/TIP.2023.3305090","article-title":"FsaNet: Frequency self-attention for semantic segmentation","volume":"32","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10.1016\/j.patcog.2026.113988_b30","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.3390\/foods14081306","article-title":"Lightweight DeepLabv3+ for semantic food segmentation","volume":"14","author":"Mu\u00f1oz","year":"2025","journal-title":"Foods"},{"key":"10.1016\/j.patcog.2026.113988_b31","doi-asserted-by":"crossref","unstructured":"L. Chen, L. Gu, D. Zheng, Y. Fu, Frequency-adaptive dilated convolution for semantic segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 3414\u20133425.","DOI":"10.1109\/CVPR52733.2024.00328"},{"key":"10.1016\/j.patcog.2026.113988_b32","series-title":"2023 IEEE International Conference on Computing","first-page":"462","article-title":"Performance assessment of U-Net for semantic segmentation of liquid spray images with Gaussian blurring","author":"Lim","year":"2023"},{"key":"10.1016\/j.patcog.2026.113988_b33","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8825","article-title":"Benchmarking the robustness of semantic segmentation models","author":"Kamann","year":"2020"},{"key":"10.1016\/j.patcog.2026.113988_b34","doi-asserted-by":"crossref","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"12","key":"10.1016\/j.patcog.2026.113988_b35","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1007\/s13042-020-01153-z","article-title":"Benchmarking algorithms for food localization and semantic segmentation","volume":"11","author":"Aslan","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.patcog.2026.113988_b36","series-title":"SIGGRAPH Asia 2012 Technical Briefs","article-title":"Automatic Chinese food identification and quantity estimation","author":"Chen","year":"2012"},{"key":"10.1016\/j.patcog.2026.113988_b37","series-title":"European Conference on Computer Vision","first-page":"369","article-title":"Increasing the robustness of semantic segmentation models with painting-by-numbers","author":"Kamann","year":"2020"},{"key":"10.1016\/j.patcog.2026.113988_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111975","article-title":"Refining pseudo-labels through iterative mix-up for weakly supervised semantic segmentation","volume":"169","author":"Wang","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113988_b39","series-title":"International Conference on Pattern Recognition and Artificial Intelligence","first-page":"3","article-title":"Transparency distortion robustness for sota image segmentation tasks","author":"Knauthe","year":"2024"},{"key":"10.1016\/j.patcog.2026.113988_b40","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16896","article-title":"Bending reality: Distortion-aware transformers for adapting to panoramic semantic segmentation","author":"Zhang","year":"2022"},{"key":"10.1016\/j.patcog.2026.113988_b41","doi-asserted-by":"crossref","unstructured":"A. Rajagopalan, et al., Improving robustness of semantic segmentation to motion-blur using class-centric augmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 10470\u201310479.","DOI":"10.1109\/CVPR52729.2023.01009"},{"key":"10.1016\/j.patcog.2026.113988_b42","doi-asserted-by":"crossref","unstructured":"T. Xiao, Y. Liu, B. Zhou, Y. Jiang, J. Sun, Unified perceptual parsing for scene understanding, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 418\u2013434.","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"10.1016\/j.patcog.2026.113988_b43","doi-asserted-by":"crossref","unstructured":"X. Wu, X. Fu, Y. Liu, E.-P. Lim, S.C. Hoi, Q. Sun, A large-scale benchmark for food image segmentation, in: Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 506\u2013515.","DOI":"10.1145\/3474085.3475201"},{"key":"10.1016\/j.patcog.2026.113988_b44","series-title":"Pattern Recognition. ICPR International Workshops and Challenges:1 Virtual Event, January 10\u201315, 2021, Proceedings, Part V","first-page":"647","article-title":"Uec-foodpix complete: A large-scale food image segmentation dataset","author":"Okamoto","year":"2021"},{"key":"10.1016\/j.patcog.2026.113988_b45","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.patcog.2026.113988_b46","doi-asserted-by":"crossref","unstructured":"S.R. Oota, V. Rowtula, S. Mohammed, M. Liu, M. Gupta, WSNet: towards an effective method for wound image segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 3234\u20133243.","DOI":"10.1109\/WACV56688.2023.00325"},{"key":"10.1016\/j.patcog.2026.113988_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"10.1016\/j.patcog.2026.113988_b48","doi-asserted-by":"crossref","unstructured":"A. Kirillov, R. Girshick, K. He, P. Doll\u00e1r, Panoptic feature pyramid networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 6399\u20136408.","DOI":"10.1109\/CVPR.2019.00656"},{"key":"10.1016\/j.patcog.2026.113988_b49","doi-asserted-by":"crossref","unstructured":"Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, W. Liu, Ccnet: Criss-cross attention for semantic segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 603\u2013612.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"10.1016\/j.patcog.2026.113988_b50","doi-asserted-by":"crossref","unstructured":"W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, J. Feng, S. Yan, Metaformer is actually what you need for vision, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10819\u201310829.","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"10.1016\/j.patcog.2026.113988_b51","article-title":"Foodsam: Any food segmentation","author":"Lan","year":"2023","journal-title":"IEEE Trans. Multimed."},{"issue":"1","key":"10.1016\/j.patcog.2026.113988_b52","first-page":"1","article-title":"Food image segmentation based on deep and shallow dual-branch network","volume":"31","author":"Xiao","year":"2025","journal-title":"Multimedia Syst."},{"key":"10.1016\/j.patcog.2026.113988_b53","doi-asserted-by":"crossref","unstructured":"S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P.H. Torr, et al., Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 6881\u20136890.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"10.1016\/j.patcog.2026.113988_b54","doi-asserted-by":"crossref","unstructured":"J. Xu, Z. Xiong, S.P. Bhattacharyya, PIDNet: A Real-Time Semantic Segmentation Network Inspired by PID Controllers, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 19529\u201319539.","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"10.1016\/j.patcog.2026.113988_b55","doi-asserted-by":"crossref","unstructured":"M. Fan, S. Lai, J. Huang, X. Wei, Z. Chai, J. Luo, X. Wei, Rethinking bisenet for real-time semantic segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 9716\u20139725.","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"10.1016\/j.patcog.2026.113988_b56","doi-asserted-by":"crossref","unstructured":"A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, et al., Searching for mobilenetv3, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"3","key":"10.1016\/j.patcog.2026.113988_b57","doi-asserted-by":"crossref","first-page":"3448","DOI":"10.1109\/TITS.2022.3228042","article-title":"Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes","volume":"24","author":"Pan","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.patcog.2026.113988_b58","first-page":"1140","article-title":"Segnext: Rethinking convolutional attention design for semantic segmentation","volume":"35","author":"Guo","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113988_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109047","article-title":"Polar contrast attention and skip cross-channel aggregation for efficient learning in U-Net","volume":"181","author":"Lawal","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.patcog.2026.113988_b60","doi-asserted-by":"crossref","unstructured":"J.-H. Nam, N.S. Syazwany, S.J. Kim, S.-C. Lee, Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 11480\u201311491.","DOI":"10.1109\/CVPR52733.2024.01091"},{"issue":"1","key":"10.1016\/j.patcog.2026.113988_b61","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009532?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326009532?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:57:24Z","timestamp":1780930644000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326009532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":61,"alternative-id":["S0031320326009532"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113988","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Frequency aided attention mechanism for better segmentation generalization and explainability","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113988","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"113988"}}