El-Hariri H, Neto LASM, Cimflova P, Bala F. Evaluating nnu-uet for early ischemic change segmentation on non-contrast computed tomography in patients with acute ischemic stroke. Comput Biol Med. 2022;141: 105033.

Article  Google Scholar 

Khezrpour S, Seyedarabi H, Razavi SN, Farhoudi M. Automatic segmentation of the brain stroke lesions from MR flair scans using improved U-net framework. Biomed Signal Process Control. 2022;78:103978.

Article  Google Scholar 

Wilson G, Cook DJ. A survey of unsupervised deep domain adaptation. ACM Trans Intell Syst Technol. 2020;11:1–46.

Article  Google Scholar 

Fang Y, Yap P-T, Lin W, Zhu H, Liu M. Source-free unsupervised domain adaptation: a survey 2022. arXiv preprint. Available from: arXiv:2301.00265

Gawlikowski J, Tassi CRN, Ali M, Lee J, Humt M, Feng J, Kruspe A, Triebel R, Jung P, Roscher R, et al. A survey of uncertainty in deep neural networks 2021. arXiv preprint. Available from:arXiv:2107.03342

Lai Y, Shi Y, Han Y, Shao Y, Qi M, Li B. Exploring uncertainty in deep learning for construction of prediction intervals 2021. arXiv preprint. Available from: arXiv:2104.12953

Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer. 2022;22(2):114–26.

Article  Google Scholar 

Cui C, Yang H, Wang Y, Zhao S, Asad Z, Coburn LA, Wilson KT, Landman B, Huo Y. Deep multi-modal fusion of image and non-image data in disease diagnosis and prognosis: a review. Prog Biomed Eng. 2023;5(13):52–65.

Google Scholar 

Guan H, Liu M. Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng. 2022;69(3):1173–85.

Article  Google Scholar 

Yang C, Guo X, Chen Z, Yuan Y. Source free domain adaptation for medical image segmentation with fourier style mining. Med Image Anal. 2022;79: 102457.

Article  Google Scholar 

Bateson M, Kervadec H, Dolz J, Lombaert H, Ayed IB. Source-free domain adaptation for image segmentation. Med Image Anal. 2022;82:102617.

Article  Google Scholar 

Liu X, Yuan Y. A source-free domain adaptive polyp detection framework with style diversification flow. IEEE Trans Med Imaging. 2022;41(7):1897–908.

Article  Google Scholar 

Kondo S. Source-free unsupervised domain adaptation with norm and shape constraints for medical image segmentation 2022. arXiv preprint. Available from: arXiv:2209.01300

Bateson M, Kervadec H, Dolz J, Lombaert H, Ben Ayed I. Source-relaxed domain adaptation for image segmentation. In: Medical image computing and computer assisted intervention. Cham: Springer; 2020. p. 490–9.

Google Scholar 

VS V, Valanarasu JMJ, Patel VM. Target and task specific source-free domain adaptive image segmentation 2022. arXiv preprint. Available from: arXiv:2203.15792

Xu Z, Lu D, Wang Y, Luo J, Wei D, Zheng Y, Tong RKY. Denoising for relaxing: unsupervised domain adaptive fundus image segmentation without sourcedata. In: Medical image computing and computer assisted intervention. Cham: Springer; 2022. p. 214–24.

Google Scholar 

Liu L, Kurgan L, Wu F-X, Wang J. Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal. 2020;65:101791.

Article  Google Scholar 

Dolz J, Desrosiers C, Ben Ayed I. IVD-Net: intervertebral disc localization and segmentation in MRI with a multi-modal UNet. In: Medical image computing and computer assisted intervention. Cham: Springer; 2019. p. 130–43.

Google Scholar 

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017; pp. 4700–4708.

Kamnitsas K, Bai W, Ferrante E, McDonagh S, Sinclair M, Pawlowski N, Rajchl M, Lee M, Kainz B, Rueckert D, Glocker B. Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Berlin: Springer; 2018. p. 450–65.

Chapter  Google Scholar 

Han Z, Zhang C, Fu H, Zhou JT. Trusted multi-view classification with dynamic evidential fusion. IEEE Trans Pattern Anal Mach Intell. 2023;45(2):2551–66.

Article  Google Scholar 

Xu S, Chen Y, Ma C, Yue X. Deep evidential fusion network for medical image classification. Int J Approx Reason. 2022;150:188–98.

Article  MathSciNet  MATH  Google Scholar 

Rizve MN, Duarte K, Rawat YS, Shah M. In: Defense of pseudo-labeling: an uncertainty-aware pseudo-label selection framework for semi-supervised learning 2021. arXiv preprint. Available from: arXiv:2101.06329

Chen C, Liu Q, Jin Y, Dou Q, Heng P-A. Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling. In: Medical Image Computing and Computer Assisted Intervention, 2021;pp. 225–35.

Gal Y, Ghahramani Z. Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, 2016; pp. 1050–1059.

Zheng Z, Yang Y. Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. Int J Comput Vis. 2021;129(4):1106–20.

Article  Google Scholar 

Van Amersfoort J, Smith L, Teh YW, Gal Y. Uncertainty estimation using a single deep deterministic neural network. Int Conf Mach Learn. 2020;119:9690–700.

Google Scholar 

Zheng H, Chen Y, Yue X, Ma C, Liu X, Yang P, Lu J. Deep pancreas segmentation with uncertain regions of shadowed sets. Magn Reson Imaging. 2020;68:45–52.

Article  Google Scholar 

Tong Z, Xu P, Denœux T. An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing. 2021;450:275–93.

Article  Google Scholar 

Sensoy M, Kaplan L, Kandemir M. Evidential deep learning to quantify classification uncertainty. In: Proceedings of the 32nd international conference on neural information processing system, 2018; pp. 3183–3193.

Dempster AP. Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika. 1967;54(3–4):515–28.

Article  MathSciNet  Google Scholar 

Jsang A. Subjective Logic: a formalism for reasoning under uncertainty. Berlin: Springer; 2018.

Google Scholar 

Ghesu FC, Georgescu B, Gibson E, Guendel S, Kalra MK, Singh R, Digumarthy SR, Grbic S, Comaniciu D. Quantifying and leveraging classification uncertainty for chest radiograph assessment. In: Medical image computing and computer assisted intervention. Berlin: Springer; 2019. p. 676–84.

Google Scholar 

Shafer G. A mathematical theory of evidence. Priceton: Princeton University Press; 1976. p. 42.

Book  MATH  Google Scholar 

Li H, Nan Y, Del Ser J, Yang G. Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. Neural Comput Appl. 2022;1:1–15.

Google Scholar 

Pedrycz W. Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybern. 1998;28(1):103–9.

Article  Google Scholar 

Hernandez Petzsche MR, Rosa E, Hanning U, Wiest R, Valenzuela W, Reyes M, Meyer M, Liew S-L, Kofler F, Ezhov I, et al. ISLES 2022: a multi-center magnetic resonance imaging stroke lesion segmentation dataset. Sci Data. 2022;9(1):762.

Article  Google Scholar 

Maier O, Menze BH, Gablentz J, Häni L, Heinrich MP, et al. ISLES 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal. 2017;35:250–69.

Article  Google Scholar 

Marstal K, Berendsen F, Staring M, Klein S. Simpleelastix: a user-friendly, multi-lingual library for medical image registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2016; pp. 134–142.

Karthik R, Gupta U, Jha A, Menaka R. A deep supervised approach for ischemic lesion segmentation from multimodal MRI using fully convolutional network. App Soft Comput. 2019;84: 105685.

Article  Google Scholar 

Wang S, Yu L, Li K, Yang X, Fu C-W, Heng P-A. Boundary and entropy-driven adversarial learning for fundus image segmentation. In: Medical image computing and computer assisted intervention. Berlin: Springer; 2019. p. 102–10.

Google Scholar 

Wang D, Shelhamer E, Liu S, Olshausen B, Darrell T. Tent: fully test-time adaptation by entropy minimization 2020. arXiv preprint. Available from: arXiv:2006.10726

Yushkevich PA, Piven J, Cody Hazlett H, Gimpel Smith R, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116–28.

Article  Google Scholar