{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:01:27Z","timestamp":1780495287745,"version":"3.54.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00521-025-11459-0","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T05:13:28Z","timestamp":1754975608000},"page":"20639-20662","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An explainable deep learning-based panoptic segmentation for brain tumor diagnosis"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0165-0331","authenticated-orcid":false,"given":"Berlin","family":"Shaheema","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8499-1918","authenticated-orcid":false,"given":"Naresh Babu","family":"Muppalaneni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. Suganya","family":"Devi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"11459_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34372-9","volume-title":"Computer vision: algorithms and applications","author":"R Szeliski","year":"2022","unstructured":"Szeliski R (2022) Computer vision: algorithms and applications. Springer"},{"key":"11459_CR2","doi-asserted-by":"crossref","unstructured":"Louis DN, et al (2016) World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803\u2013820","DOI":"10.1007\/s00401-016-1545-1"},{"key":"11459_CR3","unstructured":"Schapira MWDML, M. William Dale (2021) Brain tumor: Statistics"},{"key":"11459_CR4","doi-asserted-by":"publisher","first-page":"110329","DOI":"10.1016\/j.patcog.2024.110329","volume":"150","author":"Y Guo","year":"2024","unstructured":"Guo Y et al (2024) DSCA: A Dual Semantic Correlation Alignment Method for domain adaptation object detection. Pattern Recogn 150:110329","journal-title":"Pattern Recogn"},{"key":"11459_CR5","doi-asserted-by":"crossref","unstructured":"Kirillov A, et al (2019) Panoptic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00963"},{"key":"11459_CR6","doi-asserted-by":"publisher","first-page":"107515","DOI":"10.1016\/j.asoc.2021.107515","volume":"109","author":"X Hua","year":"2021","unstructured":"Hua X et al (2021) Cascaded panoptic segmentation method for high-resolution remote sensing image. Appl Soft Comput 109:107515","journal-title":"Appl Soft Comput"},{"issue":"6","key":"11459_CR7","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1109\/TCSVT.2020.3020257","volume":"31","author":"Q Chen","year":"2020","unstructured":"Chen Q et al (2020) Spatialflow: bridging all tasks for panoptic segmentation. IEEE Trans Circuits Syst Video Technol 31(6):2288\u20132300","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11459_CR8","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1109\/TIP.2021.3050668","volume":"30","author":"D Liu","year":"2021","unstructured":"Liu D et al (2021) Panoptic feature fusion net: a novel instance segmentation paradigm for biomedical and biological images. IEEE Trans Image Process 30:2045\u20132059","journal-title":"IEEE Trans Image Process"},{"key":"11459_CR9","unstructured":"Muppalaneni NB (2022) Panoptic image segmentation through unet combined with melody search optimization algorithm for the realistic scene image understanding. In: IEEE international conference for women in innovation. IEEE, Technology & Entrepreneurship (ICWITE), p 2022"},{"key":"11459_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.heliyon.2024.e31017","volume":"10","author":"SM Naguib","year":"2024","unstructured":"Naguib SM et al (2024) Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network. Heliyon 10:10","journal-title":"Heliyon"},{"key":"11459_CR11","doi-asserted-by":"publisher","first-page":"1180773","DOI":"10.3389\/fmed.2023.1180773","volume":"10","author":"BM de Vries","year":"2023","unstructured":"de Vries BM et al (2023) Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Front Med 10:1180773","journal-title":"Front Med"},{"key":"11459_CR12","doi-asserted-by":"publisher","first-page":"102684","DOI":"10.1016\/j.media.2022.102684","volume":"84","author":"W Jin","year":"2023","unstructured":"Jin W et al (2023) Guidelines and evaluation of clinical explainable AI in medical image analysis. Med Image Anal 84:102684","journal-title":"Med Image Anal"},{"key":"11459_CR13","doi-asserted-by":"publisher","first-page":"106156","DOI":"10.1016\/j.compbiomed.2022.106156","volume":"150","author":"M Bhandari","year":"2022","unstructured":"Bhandari M et al (2022) Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Comput Biol Med 150:106156","journal-title":"Comput Biol Med"},{"key":"11459_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-01332-6","volume":"20","author":"J Amann","year":"2020","unstructured":"Amann J et al (2020) Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 20:1\u20139","journal-title":"BMC Med Inform Decis Mak"},{"key":"11459_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110786","volume":"162","author":"K Borys","year":"2023","unstructured":"Borys K et al (2023) Explainable AI in medical imaging: an overview for clinical practitioners Beyond saliency-based XAI approaches. Eur J Radiol 162:110786","journal-title":"Eur J Radiol"},{"issue":"7","key":"11459_CR16","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.3390\/diagnostics13071273","volume":"13","author":"SM Naguib","year":"2023","unstructured":"Naguib SM et al (2023) Classification of cervical spine fracture and dislocation using refined pre-trained deep model and saliency map. Diagnostics 13(7):1273","journal-title":"Diagnostics"},{"key":"11459_CR17","doi-asserted-by":"publisher","first-page":"106619","DOI":"10.1016\/j.compbiomed.2023.106619","volume":"154","author":"FH Yagin","year":"2023","unstructured":"Yagin FH et al (2023) Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput Biol Med 154:106619","journal-title":"Comput Biol Med"},{"key":"11459_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2023.101286","volume":"40","author":"SS Band","year":"2023","unstructured":"Band SS et al (2023) Application of explainable artificial intelligence in medical health: a systematic review of interpretability methods. Informat Med Unlocked 40:101286","journal-title":"Informat Med Unlocked"},{"issue":"9","key":"11459_CR19","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1007\/s11548-022-02619-x","volume":"17","author":"RA Zeineldin","year":"2022","unstructured":"Zeineldin RA et al (2022) Explainability of deep neural networks for MRI analysis of brain tumors. Int J Comput-Assist Radiol Surg 17(9):1673\u20131683","journal-title":"Int J Comput-Assist Radiol Surg"},{"key":"11459_CR20","doi-asserted-by":"publisher","first-page":"3281998","DOI":"10.1155\/2023\/3281998","volume":"2023","author":"MA Kassem","year":"2023","unstructured":"Kassem MA et al (2023) Explainable transfer learning-based deep learning model for pelvis fracture detection. Int J Intell Syst 2023:3281998","journal-title":"Int J Intell Syst"},{"key":"11459_CR21","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3389\/fncom.2020.00006","volume":"14","author":"P Natekar","year":"2020","unstructured":"Natekar P, Kori A, Krishnamurthi G (2020) Demystifying brain tumor segmentation networks: interpretability and uncertainty analysis. Front Comput Neurosci 14:6","journal-title":"Front Comput Neurosci"},{"key":"11459_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104410","volume":"133","author":"H Saleem","year":"2021","unstructured":"Saleem H, Shahid AR, Raza B (2021) Visual interpretability in 3D brain tumor segmentation network. Comput Biol Med 133:104410","journal-title":"Comput Biol Med"},{"key":"11459_CR23","doi-asserted-by":"publisher","first-page":"58533","DOI":"10.1109\/ACCESS.2020.2983075","volume":"8","author":"J Zhang","year":"2020","unstructured":"Zhang J et al (2020) Attention gate resU-Net for automatic MRI brain tumor segmentation. IEEE Access 8:58533\u201358545","journal-title":"IEEE Access"},{"key":"11459_CR24","doi-asserted-by":"publisher","first-page":"102458","DOI":"10.1016\/j.bspc.2021.102458","volume":"66","author":"G Karayegen","year":"2021","unstructured":"Karayegen G, Aksahin MF (2021) Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region. Biomed Signal Process Control 66:102458","journal-title":"Biomed Signal Process Control"},{"key":"11459_CR25","doi-asserted-by":"publisher","first-page":"22951","DOI":"10.1007\/s11042-020-08795-9","volume":"80","author":"F Lin","year":"2021","unstructured":"Lin F et al (2021) Path aggregation U-Net model for brain tumor segmentation. Multimed Tools Appl 80:22951\u201322964","journal-title":"Multimed Tools Appl"},{"key":"11459_CR26","doi-asserted-by":"publisher","first-page":"103077","DOI":"10.1016\/j.bspc.2021.103077","volume":"71","author":"D Maji","year":"2022","unstructured":"Maji D, Sigedar P, Singh M (2022) Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors. Biomed Signal Process Control 71:103077","journal-title":"Biomed Signal Process Control"},{"key":"11459_CR27","doi-asserted-by":"publisher","first-page":"103647","DOI":"10.1016\/j.bspc.2022.103647","volume":"76","author":"N Cinar","year":"2022","unstructured":"Cinar N, Ozcan A, Kaya M (2022) A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images. Biomed Signal Process Control 76:103647","journal-title":"Biomed Signal Process Control"},{"key":"11459_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103861","volume":"79","author":"R Raza","year":"2023","unstructured":"Raza R et al (2023) dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. Biomed Signal Process Control 79:103861","journal-title":"Biomed Signal Process Control"},{"issue":"22","key":"11459_CR29","doi-asserted-by":"publisher","first-page":"20191","DOI":"10.1007\/s00521-022-07583-w","volume":"34","author":"W Elhamzi","year":"2022","unstructured":"Elhamzi W, Ayadi W, Atri M (2022) A novel automatic approach for glioma segmentation. Neural Comput Appl 34(22):20191\u201320201","journal-title":"Neural Comput Appl"},{"issue":"4","key":"11459_CR30","first-page":"709","volume":"28","author":"T Yang","year":"2020","unstructured":"Yang T et al (2020) DCU-Net: Multi-scale U-Net for brain tumor segmentation. J Xray Sci Technol 28(4):709\u2013726","journal-title":"J Xray Sci Technol"},{"key":"11459_CR31","doi-asserted-by":"publisher","first-page":"104336","DOI":"10.1016\/j.bspc.2022.104336","volume":"80","author":"Y Peng","year":"2023","unstructured":"Peng Y, Sun J (2023) The multimodal MRI brain tumor segmentation based on AD-Net. Biomed Signal Process Control 80:104336","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"11459_CR32","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/JBHI.2023.3271808","volume":"27","author":"H Yang","year":"2023","unstructured":"Yang H et al (2023) Flexible fusion network for multimodal brain tumor segmentation. IEEE J Biomed Health Informat 27(7):3349\u20133359","journal-title":"IEEE J Biomed Health Informat"},{"issue":"3","key":"11459_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450519","volume":"21","author":"HX Hu","year":"2021","unstructured":"Hu HX et al (2021) Multimodal brain tumor segmentation based on an intelligent UNET-LSTM algorithm in smart hospitals. ACM Trans Internet Technol 21(3):1\u201314","journal-title":"ACM Trans Internet Technol"},{"issue":"3","key":"11459_CR34","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.12928\/telkomnika.v18i3.14753","volume":"18","author":"AA Pravitasari","year":"2020","unstructured":"Pravitasari AA et al (2020) UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA 18(3):1310\u20131318","journal-title":"TELKOMNIKA"},{"key":"11459_CR35","doi-asserted-by":"publisher","first-page":"832824","DOI":"10.3389\/fnins.2022.832824","volume":"16","author":"S Chen","year":"2022","unstructured":"Chen S, Zhao S, Lan Q (2022) Residual block based nested U-type architecture for multimodal brain tumor image segmentation. Front Neurosci 16:832824","journal-title":"Front Neurosci"},{"key":"11459_CR36","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.neucom.2022.05.050","volume":"500","author":"I Mazumdar","year":"2022","unstructured":"Mazumdar I, Mukherjee J (2022) Fully automatic MRI brain tumor segmentation using efficient spatial attention convolutional networks with composite loss. Neurocomputing 500:243\u2013254","journal-title":"Neurocomputing"},{"issue":"6","key":"11459_CR37","doi-asserted-by":"publisher","first-page":"4739","DOI":"10.1007\/s00521-022-07934-7","volume":"35","author":"T Balamurugan","year":"2023","unstructured":"Balamurugan T, Gnanamanoharan E (2023) Brain tumor segmentation and classification using hybrid deep CNN with LuNetClassifier. Neural Comput Appl 35(6):4739\u20134753","journal-title":"Neural Comput Appl"},{"key":"11459_CR38","doi-asserted-by":"publisher","first-page":"102307","DOI":"10.1016\/j.compmedimag.2023.102307","volume":"110","author":"X Liu","year":"2023","unstructured":"Liu X, Yao C, Chen H, Xiang R, Wu H, Du P, Geng D (2023) BTSC-TNAS: a neural architecture search-based transformer for brain tumor segmentation and classification. Comput Med Imaging Graph 110:102307","journal-title":"Comput Med Imaging Graph"},{"key":"11459_CR39","doi-asserted-by":"publisher","first-page":"122347","DOI":"10.1016\/j.eswa.2023.122347","volume":"238","author":"A Akter","year":"2024","unstructured":"Akter A, Nosheen N, Ahmed S, Hossain M, Yousuf MA, Almoyad M, Moni MA (2024) Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor. Expert Syst Appl 238:122347","journal-title":"Expert Syst Appl"},{"key":"11459_CR40","doi-asserted-by":"publisher","first-page":"110553","DOI":"10.1016\/j.patcog.2024.110553","volume":"153","author":"Z Zhu","year":"2024","unstructured":"Zhu Z et al (2024) Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction. Pattern Recogn 153:110553","journal-title":"Pattern Recogn"},{"key":"11459_CR41","doi-asserted-by":"publisher","first-page":"111624","DOI":"10.1016\/j.asoc.2024.111624","volume":"159","author":"KM Hosny","year":"2024","unstructured":"Hosny KM et al (2024) Explainable deep inherent learning for multi-classes skin lesion classification. Appl Soft Comput 159:111624","journal-title":"Appl Soft Comput"},{"key":"11459_CR42","doi-asserted-by":"crossref","unstructured":"Liu L, Yin Y (2023) Towards explainable AI on chest X-ray diagnosis using image segmentation and CAM visualization. In: Future of information and communication conference. Springer, Cham","DOI":"10.1007\/978-3-031-28076-4_48"},{"key":"11459_CR43","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1007\/s00234-020-02465-1","volume":"62","author":"P Windisch","year":"2020","unstructured":"Windisch P et al (2020) Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology 62:1515\u20131518","journal-title":"Neuroradiology"},{"issue":"11","key":"11459_CR44","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.3390\/jpm11111213","volume":"11","author":"M Esmaeili","year":"2021","unstructured":"Esmaeili M et al (2021) Explainable artificial intelligence for human\u2013machine interaction in brain tumor localization. J Pers Med 11(11):1213","journal-title":"J Pers Med"},{"issue":"6","key":"11459_CR45","doi-asserted-by":"publisher","first-page":"3438","DOI":"10.3390\/app13063438","volume":"13","author":"F Yan","year":"2023","unstructured":"Yan F et al (2023) An explainable brain tumor detection framework for MRI analysis. Appl Sci 13(6):3438","journal-title":"Appl Sci"},{"issue":"1","key":"11459_CR46","doi-asserted-by":"publisher","first-page":"3713","DOI":"10.1038\/s41598-024-54186-7","volume":"14","author":"RA Zeineldin","year":"2024","unstructured":"Zeineldin RA et al (2024) Explainable hybrid vision transformers and convolutional network for multimodal glioma segmentation in brain MRI. Sci Rep 14(1):3713","journal-title":"Sci Rep"},{"key":"11459_CR47","doi-asserted-by":"publisher","first-page":"128058","DOI":"10.1016\/j.neucom.2024.128058","volume":"599","author":"M Hassan","year":"2024","unstructured":"Hassan M et al (2024) Unfolding explainable AI for brain tumor segmentation. Neurocomputing 599:128058","journal-title":"Neurocomputing"},{"key":"11459_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106668","volume":"156","author":"S Nazir","year":"2023","unstructured":"Nazir S, Dickson DM, Akram MU (2023) Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Comput Biol Med 156:106668","journal-title":"Comput Biol Med"},{"issue":"1","key":"11459_CR49","doi-asserted-by":"publisher","first-page":"25434","DOI":"10.1038\/s41598-024-75549-0","volume":"14","author":"SM Naguib","year":"2024","unstructured":"Naguib SM et al (2024) A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images. Sci Rep 14(1):25434","journal-title":"Sci Rep"},{"key":"11459_CR50","doi-asserted-by":"crossref","unstructured":"Kapdi Rupal et al (2024) Interpretability of segmentation and overall survival for brain tumors. Explainable AI in healthcare. Chapman and Hall\/CRC, pp 111\u2013130","DOI":"10.1201\/9781003333425-6"},{"key":"11459_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00330-023-09710-0","volume":"33","author":"H Shin","year":"2023","unstructured":"Shin H et al (2023) Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI. Eur Radiol 33:1\u201312","journal-title":"Eur Radiol"},{"issue":"5","key":"11459_CR52","doi-asserted-by":"publisher","first-page":"859","DOI":"10.3390\/diagnostics13050859","volume":"13","author":"B Ta\u015fc\u0131","year":"2023","unstructured":"Ta\u015fc\u0131 B (2023) Attention Deep feature extraction from brain MRIs in explainable mode: DGXAINet. Diagnostics 13(5):859","journal-title":"Diagnostics"},{"key":"11459_CR53","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3266614","author":"S Hossain","year":"2023","unstructured":"Hossain S et al (2023) Vision transformers, ensemble model, and transfer learning leveraging explainable AI for brain tumor detection and classification. IEEE J Biomed Health Informat. https:\/\/doi.org\/10.1109\/JBHI.2023.3266614","journal-title":"IEEE J Biomed Health Informat"},{"key":"11459_CR54","doi-asserted-by":"publisher","first-page":"15975","DOI":"10.1007\/s00521-019-04679-8","volume":"32","author":"M Sharif","year":"2020","unstructured":"Sharif M et al (2020) Brain tumor detection based on extreme learning. Neural Comput Appl 32:15975\u201315987","journal-title":"Neural Comput Appl"},{"key":"11459_CR55","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.nicl.2014.08.008","volume":"6","author":"RT Shinohara","year":"2014","unstructured":"Shinohara RT et al (2014) Statistical normalization techniques for magnetic resonance imaging. NeuroImage Clin 6:9\u201319","journal-title":"NeuroImage Clin"},{"key":"11459_CR56","doi-asserted-by":"publisher","unstructured":"Liu S, Qi L, Qin HF, Shi JP, Jia JY (2018) Path aggregation network for instance segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 18-23 June 2018, pp 8759-8768. https:\/\/doi.org\/10.1109\/CVPR.2018.00913","DOI":"10.1109\/CVPR.2018.00913"},{"key":"11459_CR57","doi-asserted-by":"publisher","first-page":"106271","DOI":"10.1016\/j.engappai.2023.106271","volume":"123","author":"Y Guo","year":"2023","unstructured":"Guo Y et al (2023) THFE: a triple-hierarchy Feature Enhancement method for tiny boat detection. Eng Appl Artif Intell 123:106271","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"11459_CR58","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s40537-023-00769-6","volume":"10","author":"YS Alsahafi","year":"2023","unstructured":"Alsahafi YS, Kassem MA, Hosny KM (2023) Skin-Net: a novel deep residual network for skin lesions classification using multilevel feature extraction and cross-channel correlation with detection of outlier. J Big Data 10(1):105","journal-title":"J Big Data"},{"key":"11459_CR59","doi-asserted-by":"crossref","unstructured":"Koonce B, Koonce B (2021) ResNet 50. Image recognition and dataset categorization, convolutional neural networks with swift for tensorflow, pp 63\u201372","DOI":"10.1007\/978-1-4842-6168-2_6"},{"key":"11459_CR60","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/LSP.2021.3051845","volume":"28","author":"Y Li","year":"2021","unstructured":"Li Y et al (2021) EACNet: enhanced asymmetric convolution for real-time semantic segmentation. IEEE Signal Process Lett 28:234\u2013238","journal-title":"IEEE Signal Process Lett"},{"key":"11459_CR61","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2019.01.103","volume":"338","author":"G Wang","year":"2019","unstructured":"Wang G et al (2019) Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338:34\u201345","journal-title":"Neurocomputing"},{"key":"11459_CR62","doi-asserted-by":"crossref","unstructured":"Mehta R, et al (2022) QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results. J Mach Learn Miomed Imaging 2022","DOI":"10.59275\/j.melba.2022-354b"},{"issue":"5","key":"11459_CR63","doi-asserted-by":"publisher","first-page":"802","DOI":"10.62110\/sciencein.jist.2024.v12.802","volume":"12","author":"B Parmar","year":"2024","unstructured":"Parmar B, Parikh M (2024) Estimation of uncertainty in brain tumor segmentation using modified multistage 3D-UNet on multimodal MRI images. J Integr Sci Technol 12(5):802","journal-title":"J Integr Sci Technol"},{"issue":"6","key":"11459_CR64","doi-asserted-by":"publisher","first-page":"1762","DOI":"10.1109\/JBHI.2019.2949601","volume":"24","author":"T He","year":"2019","unstructured":"He T et al (2019) Medimlp: using grad-cam to extract crucial variables for lung cancer postoperative complication prediction. IEEE J Biomed Health Inform 24(6):1762\u20131771","journal-title":"IEEE J Biomed Health Inform"},{"key":"11459_CR65","unstructured":"Baid U, et al (2021) The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314"},{"key":"11459_CR66","doi-asserted-by":"crossref","unstructured":"Jiang Z, Ding C, Liu M, Tao D (2019) Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task. In: BrainLes, medical image computing and computer assisted intervention (MICCAI), pp 231 241. LNCS, Springer","DOI":"10.1007\/978-3-030-46640-4_22"},{"issue":"297","key":"11459_CR67","first-page":"302","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(297):302","journal-title":"Ecology"},{"key":"11459_CR68","doi-asserted-by":"publisher","first-page":"15006","DOI":"10.1117\/1.JMI.5.1.015006","volume":"5","author":"V Yeghiazaryan","year":"2018","unstructured":"Yeghiazaryan V, Voiculescu I (2018) Family of boundary overlap metrics for the evaluation of medical image segmentation. J Med Imaging 5:15006","journal-title":"J Med Imaging"},{"key":"11459_CR69","unstructured":"Powers D, MW (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061"},{"issue":"8","key":"11459_CR70","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861\u2013874","journal-title":"Pattern Recogn Lett"},{"key":"11459_CR71","doi-asserted-by":"publisher","first-page":"103697","DOI":"10.1109\/ACCESS.2020.2998901","volume":"8","author":"F Ramzan","year":"2020","unstructured":"Ramzan F et al (2020) Volumetric segmentation of brain regions from MRI scans using 3D convolutional neural networks. IEEE Access 8:103697\u2013103709","journal-title":"IEEE Access"},{"key":"11459_CR72","unstructured":"Muppalaneni NB, Jasper J (2022) Benign and Malignant brain tumor segmentation using a melody-search optimization algorithm with an extreme softplus learning. In: 2022 IEEE Silchar subsection conference (SILCON). IEEE"},{"issue":"6","key":"11459_CR73","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.bspc.2012.04.005","volume":"7","author":"Y Chen","year":"2012","unstructured":"Chen Y et al (2012) The domain knowledge based graph-cut model for liver CT segmentation. Biomed Signal Process Control 7(6):591\u2013598","journal-title":"Biomed Signal Process Control"},{"issue":"6","key":"11459_CR74","doi-asserted-by":"publisher","first-page":"3247","DOI":"10.1016\/j.jksuci.2022.03.022","volume":"34","author":"AS Akbar","year":"2022","unstructured":"Akbar AS, Fatichah C, Suciati N (2022) Single level UNet3D with multipath residual attention block for brain tumor segmentation. J King Saud Univer-Comput Inf Sci 34(6):3247\u20133258","journal-title":"J King Saud Univer-Comput Inf Sci"},{"key":"11459_CR75","doi-asserted-by":"publisher","first-page":"104296","DOI":"10.1016\/j.bspc.2022.104296","volume":"80","author":"Y Cao","year":"2023","unstructured":"Cao Y et al (2023) MBANet: a 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images. Biomed Signal Process Control 80:104296","journal-title":"Biomed Signal Process Control"},{"issue":"16","key":"11459_CR76","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.3390\/diagnostics13162633","volume":"13","author":"R Yousef","year":"2023","unstructured":"Yousef R, Khan S, Gupta G, Albahlal BM, Alajlan SA, Ali A (2023) Bridged-U-Net-ASPP-EVO and deep learning optimization for brain tumor segmentation. Diagnostics 13(16):2633","journal-title":"Diagnostics"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11459-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11459-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11459-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T18:02:56Z","timestamp":1757008976000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11459-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,12]]},"references-count":76,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["11459"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11459-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,12]]},"assertion":[{"value":"5 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}