{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T13:21:29Z","timestamp":1788528089818,"version":"build-2803163510"},"reference-count":50,"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":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Jiangsu Province Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","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.2026.114394","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T19:58:24Z","timestamp":1783627104000},"page":"114394","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PD","title":["MMDiffuzzy: Fuzzy memory guided diffusion for uncertainty-aware multimodal fusion in WSI analysis"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5620-9362","authenticated-orcid":false,"given":"Tianyi","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3180-7347","authenticated-orcid":false,"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuepeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiashuang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114394_b1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.ejca.2020.03.013","article-title":"The prognostic value of tumour-infiltrating lymphocytes in pancreatic cancer: a systematic review and meta-analysis","volume":"132","author":"Orhan","year":"2020","journal-title":"Eur. J. Cancer"},{"issue":"1","key":"10.1016\/j.patcog.2026.114394_b2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41368-020-00092-3","article-title":"Prognostic value of tertiary lymphoid structure and tumour infiltrating lymphocytes in oral squamous cell carcinoma","volume":"12","author":"Li","year":"2020","journal-title":"Int. J. Oral Sci."},{"key":"10.1016\/j.patcog.2026.114394_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.pharmthera.2020.107753","article-title":"Tumor microenvironment as a therapeutic target in cancer","volume":"221","author":"Xiao","year":"2021","journal-title":"Pharmacol. Ther."},{"key":"10.1016\/j.patcog.2026.114394_b4","first-page":"151","article-title":"Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning","volume":"vol. 52","author":"Klauschen","year":"2018"},{"key":"10.1016\/j.patcog.2026.114394_b5","article-title":"Characterizing the survival-associated interactions between tumor-infiltrating lymphocytes and tumors from pathological images and multi-omics data","author":"Shao","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.patcog.2026.114394_b6","first-page":"327","article-title":"Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma","volume":"8","author":"Xu","year":"2022","journal-title":"J. Pathol.: Clin. Res."},{"issue":"1","key":"10.1016\/j.patcog.2026.114394_b7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.celrep.2018.03.086","article-title":"Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images","volume":"23","author":"Saltz","year":"2018","journal-title":"Cell Rep."},{"key":"10.1016\/j.patcog.2026.114394_b8","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114394_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102846","article-title":"Diffusion models in medical imaging: A comprehensive survey","author":"Kazerouni","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.114394_b10","article-title":"Unsupervised medical image translation with adversarial diffusion models","author":"Ozbey","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.114394_b11","article-title":"Mutual information guided diffusion for zero-shot cross-modality medical image translation","author":"Wang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.114394_b12","doi-asserted-by":"crossref","DOI":"10.1109\/TFUZZ.2025.3609687","article-title":"An efficient survival prediction framework based on multimodal multi-instance fuzzy optimal transport","author":"Luo","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.patcog.2026.114394_b13","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2024.3430825","article-title":"PST-Diff: achieving high-consistency stain transfer by diffusion models with pathological and structural constraints","author":"He","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.114394_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101880","article-title":"FTransCNN: Fusing transformer and a CNN based on fuzzy logic for uncertain medical image segmentation","volume":"99","author":"Ding","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114394_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102540","article-title":"FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation","volume":"112","author":"Ding","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114394_b16","series-title":"Generating radiology reports via memory-driven transformer","author":"Chen","year":"2020"},{"key":"10.1016\/j.patcog.2026.114394_b17","doi-asserted-by":"crossref","unstructured":"P.A. Moghadam, et al., A morphology focused diffusion probabilistic model for synthesis of histopathology images, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 2000\u20132009.","DOI":"10.1109\/WACV56688.2023.00204"},{"key":"10.1016\/j.patcog.2026.114394_b18","doi-asserted-by":"crossref","unstructured":"J. Liu, et al., DOLCE: A model-based probabilistic diffusion framework for limited-angle ct reconstruction, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 10498\u201310508.","DOI":"10.1109\/ICCV51070.2023.00963"},{"key":"10.1016\/j.patcog.2026.114394_b19","doi-asserted-by":"crossref","unstructured":"A. Rahman, J.M.J. Valanarasu, I. Hacihaliloglu, V.M. Patel, Ambiguous medical image segmentation using diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11536\u201311546.","DOI":"10.1109\/CVPR52729.2023.01110"},{"issue":"2","key":"10.1016\/j.patcog.2026.114394_b20","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s00259-023-06417-8","article-title":"PET image denoising based on denoising diffusion probabilistic model","volume":"51","author":"Gong","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"10.1016\/j.patcog.2026.114394_b21","series-title":"DDM2: Self-supervised diffusion MRI denoising with generative diffusion models","author":"Xiang","year":"2023"},{"key":"10.1016\/j.patcog.2026.114394_b22","article-title":"Zero-shot medical image translation via frequency-guided diffusion models","author":"Li","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"10.1016\/j.patcog.2026.114394_b23","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","article-title":"Synthetic data in machine learning for medicine and healthcare","volume":"5","author":"Chen","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.patcog.2026.114394_b24","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"99","article-title":"Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images","author":"Akrout","year":"2023"},{"issue":"8","key":"10.1016\/j.patcog.2026.114394_b25","doi-asserted-by":"crossref","first-page":"4709","DOI":"10.1109\/TFUZZ.2024.3410929","article-title":"Fmdnn: A fuzzy-guided multigranular deep neural network for histopathological image classification","volume":"32","author":"Ding","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.patcog.2026.114394_b26","doi-asserted-by":"crossref","DOI":"10.1109\/TFUZZ.2025.3573405","article-title":"ECG adaptive synthesis with multigranular fuzzy EASNN: Enhancing wearable cardiac monitoring","author":"Fu","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"9","key":"10.1016\/j.patcog.2026.114394_b27","doi-asserted-by":"crossref","first-page":"17433","DOI":"10.1109\/TNNLS.2025.3569893","article-title":"Multigranularity fuzzy autoencoder for discriminative feature selection in high-dimensional data","volume":"36","author":"Chen","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.114394_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113692","article-title":"F2CAU-Net: A dual fuzzy medical image segmentation cascade method based on fuzzy feature learning","author":"Zhou","year":"2025","journal-title":"Appl. Soft Comput."},{"issue":"9","key":"10.1016\/j.patcog.2026.114394_b29","doi-asserted-by":"crossref","first-page":"5320","DOI":"10.1109\/TFUZZ.2024.3420963","article-title":"Cascaded two-stage feature clustering and selection via separability and consistency in fuzzy decision systems","volume":"32","author":"Chen","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.patcog.2026.114394_b30","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.asoc.2019.03.043","article-title":"Divergence measure of pythagorean fuzzy sets and its application in medical diagnosis","volume":"79","author":"Xiao","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.patcog.2026.114394_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102599","article-title":"Volumetric memory network for interactive medical image segmentation","volume":"83","author":"Zhou","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.114394_b32","doi-asserted-by":"crossref","unstructured":"S. Yan, Memory-aligned knowledge graph for clinically accurate radiology image report generation, in: Proceedings of the 21st Workshop on Biomedical Language Processing, 2022, pp. 116\u2013122.","DOI":"10.18653\/v1\/2022.bionlp-1.11"},{"issue":"1","key":"10.1016\/j.patcog.2026.114394_b33","doi-asserted-by":"crossref","first-page":"5678","DOI":"10.1038\/s41467-021-25858-z","article-title":"Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging","volume":"12","author":"Perkonigg","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.patcog.2026.114394_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101962","article-title":"M-FCCL: Memory-based concept-cognitive learning for dynamic fuzzy data classification and knowledge fusion","volume":"100","author":"Guo","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114394_b35","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"398","article-title":"CoLa-Diff: Conditional latent diffusion model for multi-modal MRI synthesis","author":"Jiang","year":"2023"},{"key":"10.1016\/j.patcog.2026.114394_b36","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"95","article-title":"Diffmic: Dual-guidance diffusion network for medical image classification","author":"Yang","year":"2023"},{"key":"10.1016\/j.patcog.2026.114394_b37","series-title":"Semantic image synthesis via diffusion models","author":"Wang","year":"2022"},{"issue":"4","key":"10.1016\/j.patcog.2026.114394_b38","first-page":"4713","article-title":"Image super-resolution via iterative refinement","volume":"45","author":"Saharia","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114394_b39","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.patcog.2026.114394_b40","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.patcog.2026.114394_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105534","article-title":"HiFuse: Hierarchical multi-scale feature fusion network for medical image classification","volume":"87","author":"Huo","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.patcog.2026.114394_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107718","article-title":"CervixFormer: A Multi-scale swin transformer-Based cervical pap-Smear WSI classification framework","volume":"240","author":"Khan","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"issue":"5","key":"10.1016\/j.patcog.2026.114394_b43","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1109\/TMI.2022.3227066","article-title":"MuRCL: Multi-instance reinforcement contrastive learning for whole slide image classification","volume":"42","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.114394_b44","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"508","article-title":"Gene-induced multimodal pre-training for image-omic classification","author":"Jin","year":"2023"},{"key":"10.1016\/j.patcog.2026.114394_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102260","article-title":"A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction","volume":"126","author":"Tan","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.patcog.2026.114394_b46","article-title":"Self-normalizing neural networks","volume":"30","author":"Klambauer","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114394_b47","series-title":"International Conference on Machine Learning","first-page":"2127","article-title":"Attention-based deep multiple instance learning","author":"Ilse","year":"2018"},{"key":"10.1016\/j.patcog.2026.114394_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101789","article-title":"Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks","volume":"65","author":"Yao","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.114394_b49","doi-asserted-by":"crossref","unstructured":"R.J. Chen, et al., Multimodal co-attention transformer for survival prediction in gigapixel whole slide images, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 4015\u20134025.","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"10.1016\/j.patcog.2026.114394_b50","series-title":"M2EF-NNs: Multimodal multi-instance evidence fusion neural networks for cancer survival prediction","author":"Luo","year":"2024"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326013592?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326013592?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T12:52:31Z","timestamp":1788526351000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326013592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":50,"alternative-id":["S0031320326013592"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114394","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":"MMDiffuzzy: Fuzzy memory guided diffusion for uncertainty-aware multimodal fusion in WSI analysis","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114394","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":"114394"}}