{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T23:18:06Z","timestamp":1773616686255,"version":"3.50.1"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1016\/j.knosys.2022.108739","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T11:44:48Z","timestamp":1649331888000},"page":"108739","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":22,"special_numbering":"C","title":["A unified uncertainty network for tumor segmentation using uncertainty cross entropy loss and prototype similarity"],"prefix":"10.1016","volume":"246","author":[{"given":"Zhaoshuo","family":"Diao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1428-8776","authenticated-orcid":false,"given":"Huiyan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Tianyu","family":"Shi","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.knosys.2022.108739_b1","doi-asserted-by":"crossref","first-page":"e148","DOI":"10.1002\/mp.13649","article-title":"Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications","volume":"47","author":"Seo","year":"2020","journal-title":"Med. Phys."},{"issue":"6","key":"10.1016\/j.knosys.2022.108739_b2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"10.1016\/j.knosys.2022.108739_b3","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.knosys.2022.108739_b4","doi-asserted-by":"crossref","unstructured":"E. Shelhamer, J. Long, T. Darrell, Fully convolutional networks for semantic segmentation, 39 (4) (2017) 640\u2013651. http:\/\/dx.doi.org\/10.1109\/TPAMI.2016.2572683.","DOI":"10.1109\/TPAMI.2016.2572683"},{"issue":"12","key":"10.1016\/j.knosys.2022.108739_b5","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2022.108739_b6","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.knosys.2022.108739_b7","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","first-page":"3","article-title":"UNet++: A nested U-Net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.knosys.2022.108739_b8","series-title":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1055","article-title":"UNet 3+: A full-scale connected unet for medical image segmentation","author":"Huang","year":"2020"},{"key":"10.1016\/j.knosys.2022.108739_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.knosys.2022.108739_b10","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ins.2021.07.024","article-title":"BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification","volume":"577","author":"Abdar","year":"2021","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2022.108739_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106291","article-title":"Building robust pathology image analyses with uncertainty quantification","volume":"208","author":"Gomes","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.knosys.2022.108739_b12","series-title":"Uncertaintyfusenet: Robust uncertainty-aware hierarchical feature fusion with ensemble Monte Carlo dropout for COVID-19 detection","author":"Abdar","year":"2021"},{"key":"10.1016\/j.knosys.2022.108739_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106313","article-title":"Dilated densely connected U-Net with uncertainty focus loss for 3D ABUS mass segmentation","volume":"209","author":"Cao","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.knosys.2022.108739_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101855","article-title":"Quantifying and leveraging predictive uncertainty for medical image assessment","volume":"68","author":"Ghesu","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2022.108739_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104418","article-title":"Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning","author":"Abdar","year":"2021","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"10.1016\/j.knosys.2022.108739_b16","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1109\/TMI.2019.2948320","article-title":"Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images","volume":"39","author":"Seo","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2022.108739_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106501","article-title":"HFRU-Net: High-level feature fusion and recalibration unet for automatic liver and tumor segmentation in CT images","volume":"213","author":"Kushnure","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.knosys.2022.108739_b18","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"5580","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"Kendall","year":"2017"},{"key":"10.1016\/j.knosys.2022.108739_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101833","article-title":"Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty","volume":"67","author":"Bertels","year":"2021","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.knosys.2022.108739_b20","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","article-title":"A practical Bayesian framework for backpropagation networks","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput."},{"key":"10.1016\/j.knosys.2022.108739_b21","series-title":"Proceedings of the British Machine Vision Conference (BMVC)","first-page":"57.1","article-title":"Bayesian SegNet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding","author":"Alex\u00a0Kendall","year":"2017"},{"key":"10.1016\/j.knosys.2022.108739_b22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neuroimage.2019.03.042","article-title":"Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control","volume":"195","author":"Roy","year":"2019","journal-title":"NeuroImage"},{"key":"10.1016\/j.knosys.2022.108739_b23","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"1441","article-title":"U2-Net: A Bayesian U-net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans","author":"Orlando","year":"2019"},{"issue":"4","key":"10.1016\/j.knosys.2022.108739_b24","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/TMI.2019.2940555","article-title":"Automated muscle segmentation from clinical CT using Bayesian U-Net for personalized musculoskeletal modeling","volume":"39","author":"Hiasa","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2022.108739_b25","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","first-page":"614","article-title":"Self-loop uncertainty: A novel pseudo-label for semi-supervised medical image segmentation","author":"Li","year":"2020"},{"key":"10.1016\/j.knosys.2022.108739_b26","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","first-page":"439","article-title":"Mt-UcGAN: Multi-task uncertainty-constrained GAN for joint segmentation, quantification and uncertainty estimation of renal tumors on CT","author":"Ruan","year":"2020"},{"key":"10.1016\/j.knosys.2022.108739_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101557","article-title":"Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation","volume":"59","author":"Nair","year":"2020","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.knosys.2022.108739_b28","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1109\/TCYB.2020.2992433","article-title":"SG-one: Similarity guidance network for one-shot semantic segmentation","volume":"50","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.knosys.2022.108739_b29","series-title":"2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"9196","article-title":"PANet: Few-shot image semantic segmentation with prototype alignment","author":"Wang","year":"2019"},{"key":"10.1016\/j.knosys.2022.108739_b30","series-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"Hendrycks","year":"2018"},{"key":"10.1016\/j.knosys.2022.108739_b31","series-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","first-page":"6965","article-title":"A probabilistic U-net for segmentation of ambiguous images","author":"Kohl","year":"2018"},{"key":"10.1016\/j.knosys.2022.108739_b32","series-title":"Learning Structured Output Representation Using Deep Conditional Generative Models","first-page":"3483","author":"Sohn","year":"2015"},{"key":"10.1016\/j.knosys.2022.108739_b33","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"6405","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"Lakshminarayanan","year":"2017"},{"key":"10.1016\/j.knosys.2022.108739_b34","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"3611","article-title":"Learning in an uncertain world: Representing ambiguity through multiple hypotheses","author":"Rupprecht","year":"2017"},{"key":"10.1016\/j.knosys.2022.108739_b35","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2019.01.103","article-title":"Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks","volume":"338","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2022.108739_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101715","article-title":"DR\u2014GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images","volume":"63","author":"Ara\u00fajo","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2022.108739_b37","series-title":"Advances in Neural Information Processing Systems 32 (NIPS 2019)","first-page":"6414","article-title":"Single-model uncertainties for deep learning","author":"Tagasovska","year":"2019"},{"key":"10.1016\/j.knosys.2022.108739_b38","series-title":"Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty","author":"Monteiro","year":"2020"},{"key":"10.1016\/j.knosys.2022.108739_b39","doi-asserted-by":"crossref","first-page":"132330","DOI":"10.1109\/ACCESS.2020.3010274","article-title":"Anomalous example detection in deep learning: A survey","volume":"8","author":"Bulusu","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2022.108739_b40","series-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","first-page":"7386","article-title":"Out-of-distribution detection using multiple semantic label representations","author":"Shalev","year":"2018"},{"key":"10.1016\/j.knosys.2022.108739_b41","series-title":"2020 IEEE Security and Privacy Workshops (SPW)","first-page":"250","article-title":"Out-of-distribution detection in multi-label datasets using latent space of \u03b2-VAE","author":"Sundar","year":"2020"},{"key":"10.1016\/j.knosys.2022.108739_b42","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"5405","article-title":"SDE-Net: Equipping deep neural networks with uncertainty estimates","volume":"vol. 119","author":"Kong","year":"2020"},{"issue":"12","key":"10.1016\/j.knosys.2022.108739_b43","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: Hybrid densely connected unet for liver and tumor segmentation from CT volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2022.108739_b44","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"issue":"14","key":"10.1016\/j.knosys.2022.108739_b45","doi-asserted-by":"crossref","first-page":"5471","DOI":"10.1088\/0031-9155\/60\/14\/5471","article-title":"A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities","volume":"60","author":"Valli\u00e8res","year":"2015","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.knosys.2022.108739_b46","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","first-page":"376","article-title":"MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures","author":"Ballestar","year":"2021"},{"key":"10.1016\/j.knosys.2022.108739_b47","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.neucom.2020.06.146","article-title":"Automatic segmentation of gross target volume of nasopharynx cancer using ensemble of multiscale deep neural networks with spatial attention","volume":"438","author":"Mei","year":"2021","journal-title":"Neurocomputing"},{"issue":"10","key":"10.1016\/j.knosys.2022.108739_b48","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/TMI.2020.3048055","article-title":"Diminishing uncertainty within the training pool: Active learning for medical image segmentation","volume":"40","author":"Nath","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2022.108739_b49","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","first-page":"810","article-title":"Semi-supervised medical image segmentation via learning consistency under transformations","author":"Bortsova","year":"2019"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705122003410?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705122003410?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T04:41:31Z","timestamp":1760589691000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705122003410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6]]},"references-count":49,"alternative-id":["S0950705122003410"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2022.108739","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A unified uncertainty network for tumor segmentation using uncertainty cross entropy loss and prototype similarity","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2022.108739","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"108739"}}