{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:22:13Z","timestamp":1783106533474,"version":"3.54.6"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976126"],"award-info":[{"award-number":["61976126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.eswa.2026.132824","type":"journal-article","created":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T14:27:54Z","timestamp":1778423274000},"page":"132824","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Fusion-from-zero network with information fusion for multimodal head and neck tumor segmentation"],"prefix":"10.1016","volume":"327","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9416-0181","authenticated-orcid":false,"given":"Jiao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8444-0622","authenticated-orcid":false,"given":"Yanjun","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1652-3861","authenticated-orcid":false,"given":"Yanfei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengzhong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132824_bib0001","series-title":"3D head and neck tumor segmentation in PET\/CT challenge","first-page":"1","article-title":"Overview of the HECKTOR challenge at MICCAI 2022: automatic head and neck tumor segmentation and outcome prediction in PET\/CT","author":"Andrearczyk","year":"2022"},{"key":"10.1016\/j.eswa.2026.132824_bib0002","series-title":"3D head and neck tumor segmentation in PET\/CT challenge","first-page":"1","article-title":"Overview of the HECKTOR challenge at MICCAI 2021: Automatic head and neck tumor segmentation and outcome prediction in PET\/CT images","author":"Andrearczyk","year":"2021"},{"key":"10.1016\/j.eswa.2026.132824_bib0003","series-title":"Head and neck tumor segmentation: First challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, october 4, 2020, proceedings 1","first-page":"1","article-title":"Overview of the HECKTOR challenge at MICCAI 2020: automatic head and neck tumor segmentation in PET\/CT","author":"Andrearczyk","year":"2021"},{"key":"10.1016\/j.eswa.2026.132824_bib0004","unstructured":"Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F. C., Pati, S. et al. (2021). The RSNA-ASNR-MICCAI brats 2021 Benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv: 2107.0231410.48550\/arXiv.2107.02314."},{"issue":"1","key":"10.1016\/j.eswa.2026.132824_bib0005","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features","volume":"4","author":"Bakas","year":"2017","journal-title":"Scientific Data"},{"key":"10.1016\/j.eswa.2026.132824_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104037","article-title":"DPAFNet: A residual dual-path attention-fusion convolutional neural network for multimodal brain tumor segmentation","volume":"79","author":"Chang","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.132824_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105705","article-title":"Multimodal image feature fusion for improving medical ultrasound image segmentation","volume":"89","author":"Chen","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.132824_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.132824_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103241","article-title":"I2u-Net: A dual-path U-Net with rich information interaction for medical image segmentation","volume":"97","author":"Dai","year":"2024","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.132824_bib0010","first-page":"1","article-title":"CHITNet: A complementary to harmonious information transfer network for infrared and visible image fusion","volume":"74","author":"Du","year":"2025","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"1","key":"10.1016\/j.eswa.2026.132824_bib0011","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1038\/s41597-022-01718-3","article-title":"A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions","volume":"9","author":"Gatidis","year":"2022","journal-title":"Scientific Data"},{"key":"10.1016\/j.eswa.2026.132824_bib0012","doi-asserted-by":"crossref","unstructured":"Gatidis S, K. T. (2022). A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions (FDG-PET-CT-lesions). 10.7937\/gkr0-xv29.","DOI":"10.1038\/s41597-022-01718-3"},{"issue":"9","key":"10.1016\/j.eswa.2026.132824_bib0013","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1109\/TMI.2023.3264513","article-title":"H2Former: An efficient hierarchical hybrid transformer for medical image segmentation","volume":"42","author":"He","year":"2023","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.132824_bib0014","series-title":"Proceedings of the IEEE\/CVF Winter conference on applications of computer vision","first-page":"6202","article-title":"HiFormer: Hierarchical multi-scale representations using transformers for medical image segmentation","author":"Heidari","year":"2023"},{"issue":"2","key":"10.1016\/j.eswa.2026.132824_bib0015","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.eswa.2026.132824_bib0016","series-title":"2023\u202fIEEE\/CVF International conference on computer vision (ICCV)","first-page":"3992","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.eswa.2026.132824_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104402","article-title":"DFENet: A dual-branch feature enhanced network integrating transformers and convolutional feature learning for multimodal medical image fusion","volume":"80","author":"Li","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.132824_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121574","article-title":"TranSiam: Aggregating multi-modal visual features with locality for medical image segmentation","volume":"237","author":"Li","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132824_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110987","article-title":"MaxFormer: Enhanced transformer for medical image segmentation with multi-attention and multi-scale features fusion","volume":"280","author":"Liang","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.132824_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102634","article-title":"CSWin-UNet: Transformer unet with cross-shaped windows for medical image segmentation","volume":"113","author":"Liu","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.eswa.2026.132824_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102352","article-title":"A semantic-driven coupled network for infrared and visible image fusion","volume":"108","author":"Liu","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.eswa.2026.132824_bib0022","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1109\/TIP.2024.3374072","article-title":"MM-Net: A mixformer-based multi-scale network for anatomical and functional image fusion","volume":"33","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Image Processing"},{"issue":"1","key":"10.1016\/j.eswa.2026.132824_bib0023","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":"Nature Communications"},{"issue":"7","key":"10.1016\/j.eswa.2026.132824_bib0024","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.eswa.2026.132824_bib0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2025.3529045","article-title":"Measuring objective image and video quality: On the relationship between SSIM and PSNR for DCT-based compressed images","volume":"74","author":"Martini","year":"2025","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2026.132824_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102757","article-title":"Semi-supervised image segmentation using a residual-driven mean teacher and an exponential dice loss","volume":"148","author":"Mei","year":"2024","journal-title":"Artificial Intelligence in Medicine"},{"issue":"10","key":"10.1016\/j.eswa.2026.132824_bib0027","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.132824_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104336","article-title":"The multimodal MRI brain tumor segmentation based on AD-net","volume":"80","author":"Peng","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.132824_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105590","article-title":"Semi-supervised 3D-inceptionnet for segmentation and survival prediction of head and neck primary cancers","volume":"117","author":"Qayyum","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132824_bib0030","series-title":"Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition","first-page":"11769","article-title":"Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation","author":"Rahman","year":"2024"},{"key":"10.1016\/j.eswa.2026.132824_bib0031","first-page":"1","article-title":"HResFormer: Hybrid residual transformer for volumetric medical image segmentation","author":"Ren","year":"2025","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.132824_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110257","article-title":"Next-generation healthcare: Digital twin technology and monkeypox skin lesion detector network enhancing monkeypox detection-comparison with pre-trained models","volume":"145","author":"Sharma","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132824_bib0033","series-title":"Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition","first-page":"11248","article-title":"Vila-mil: Dual-scale vision-language multiple instance learning for whole slide image classification","author":"Shi","year":"2024"},{"key":"10.1016\/j.eswa.2026.132824_bib0034","first-page":"1","article-title":"Multimodal sentiment analysis with mutual information-based disentangled representation learning","author":"Sun","year":"2025","journal-title":"IEEE Transactions on Affective Computing"},{"key":"10.1016\/j.eswa.2026.132824_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105605","article-title":"HTC-Net: A hybrid CNN-transformer framework for medical image segmentation","volume":"88","author":"Tang","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.132824_bib0036","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","article-title":"Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network","volume":"82","author":"Tang","year":"2022","journal-title":"Information Fusion"},{"issue":"4","key":"10.1016\/j.eswa.2026.132824_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103687","article-title":"FatFusion: A functional\u2013anatomical transformer for medical image fusion","volume":"61","author":"Tang","year":"2024","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.eswa.2026.132824_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125743","article-title":"MDDU-Net: A multi-scale dense connectivity hybrid dilated convolutional u-net for segmentation in prostate ultrasound images","volume":"263","author":"Wang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132824_bib0039","first-page":"1","article-title":"MACTFusion: Lightweight cross transformer for adaptive multimodal medical image fusion","author":"Xie","year":"2024","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2026.132824_bib0040","series-title":"Proceedings of the AAAI Conference on artificial intelligence","first-page":"9202","article-title":"Pinwheel-shaped convolution and scale-based dynamic loss for infrared small target detection","volume":"vol. 39","author":"Yang","year":"2025"},{"key":"10.1016\/j.eswa.2026.132824_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122024","article-title":"Non-iterative scribble-supervised learning with pacing pseudo-masks for medical image segmentation","volume":"238","author":"Yang","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132824_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109228","article-title":"An effective CNN and transformer complementary network for medical image segmentation","volume":"136","author":"Yuan","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.132824_bib0043","first-page":"1","article-title":"PHNet: A pulmonary hypertension detection network based on cine cardiac magnetic resonance images using a hybrid strategy of adaptive triplet and binary cross-entropy losses","author":"Yuan","year":"2025","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.132824_bib0044","series-title":"Proceedings of the IEEE\/CVF International conference on computer vision","first-page":"8082","article-title":"DDFM: Denoising diffusion model for multi-modality image fusion","author":"Zhao","year":"2023"},{"issue":"4","key":"10.1016\/j.eswa.2026.132824_bib0045","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1109\/TMI.2025.3526604","article-title":"Asymmetric adaptive heterogeneous network for multi-modality medical image segmentation","volume":"44","author":"Zheng","year":"2025","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.132824_bib0046","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.inffus.2022.10.022","article-title":"Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI","volume":"91","author":"Zhu","year":"2023","journal-title":"Information Fusion"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426017379?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426017379?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:03:45Z","timestamp":1783105425000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426017379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":46,"alternative-id":["S0957417426017379"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132824","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fusion-from-zero network with information fusion for multimodal head and neck tumor segmentation","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132824","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":"132824"}}