{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:19:37Z","timestamp":1780913977125,"version":"3.54.1"},"reference-count":77,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) interpolation and segmentation are critical for clinical diagnosis, anatomical quantification and personalized treatment. Most existing methods perform these two tasks separately, leading to computational redundancy and insufficient mining of shared spatial features. This study aims to construct an integrated multi-task learning framework for the synchronous processing of medical image interpolation and segmentation. Methods: We propose a unified multi-task framework named TASC-SwinMT for joint interpolation and multi-frame segmentation of CT and MRI images. It employs a shared SwinUNet encoder to extract general spatial features, matched with two task-specific decoders for frame prediction and mask generation. Three functional modules are designed for cross-task synergistic learning, and a dynamic multi-task loss function is used to balance objective optimization. Experiments are performed on Medical Segmentation Decathlon Task02_Heart and Task06_Lung datasets. Results: Our method outperforms baseline models and ablation variants in both tasks with outstanding accuracy and significantly reduced computational overhead. It exhibits superior performance in lesion boundary depiction, small object segmentation and inter-slice consistency, and anatomical prior constraints with frequency-domain modeling further enhance prediction quality. Conclusions: The cross-task feature sharing and joint optimization strategy are validated effective. The proposed TASC-SwinMT framework has favorable stability and generalization ability, providing a reliable solution for clinical medical image analysis.<\/jats:p>","DOI":"10.3390\/tomography12060080","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T01:08:02Z","timestamp":1780535282000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TASC-SwinMT: Task-Adaptive Synergistic Cross-Task Swin Multi-Task Framework for CT and MRI Image Interpolation and Segmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8431-9156","authenticated-orcid":false,"given":"Yujia","family":"Sun","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8854-9288","authenticated-orcid":false,"given":"Yingying","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3270-4400","authenticated-orcid":false,"given":"Nan","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.jtho.2023.07.019","article-title":"Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer","volume":"19","author":"Lam","year":"2024","journal-title":"J. Thorac. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1109\/TMI.2018.2823338","article-title":"A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution","volume":"37","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1109\/TMI.2025.3558786","article-title":"Multiparametric Ultrasound Breast Tumors Diagnosis Within BI-RADS Category 4 via Feature Disentanglement and Cross-Fusion","volume":"44","author":"Ruan","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/TMI.2025.3596957","article-title":"An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolation","volume":"45","author":"Uhm","year":"2026","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Guo, Y., Bi, L., Ahn, E., Feng, D., Wang, Q., and Kim, J. (2020). A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image. arXiv.","DOI":"10.1109\/CVPR42600.2020.00478"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105667","DOI":"10.1016\/j.compbiomed.2022.105667","article-title":"Slice imputation: Multiple intermediate slices interpolation for anisotropic 3D medical image segmentation","volume":"147","author":"Wu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1186\/s40537-024-00974-x","article-title":"Interpolation-split: A data-centric deep learning approach with big interpolated data to boost airway segmentation performance","volume":"11","author":"Cheung","year":"2024","journal-title":"J. Big Data"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.patrec.2023.01.008","article-title":"SIT-SR 3D: Self-supervised slice interpolation via transfer learning for 3D volume super-resolution","volume":"166","author":"Sarmad","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.cmpb.2006.09.003","article-title":"Semi-automated CT segmentation using optic flow and Fourier interpolation techniques","volume":"84","author":"Huang","year":"2006","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cai, Y., Long, Y., Han, Z., Liu, M., Zheng, Y., Yang, W., and Chen, L. (2023). Swin Unet3D: A three-dimensional medical image segmentation network combining vision transformer and convolution. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02129-z"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., and Xu, D. (2022). Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103280","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":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s11220-025-00601-w","article-title":"CT Image Segmentation Using Frequency Domain Feature-Assisted Selective Long Memory State Space Model","volume":"26","author":"Zhang","year":"2025","journal-title":"Sens. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108732","DOI":"10.1016\/j.bspc.2025.108732","article-title":"DAGU-Net: Cascaded multi-scale aware network based on dual attention grouping module for medical image segmentation","volume":"112","author":"Liu","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109267","DOI":"10.1016\/j.bspc.2025.109267","article-title":"DCM-Net: A novel dual-branch CNN\u2013Mamba cross-layer feature fusion network for medical image segmentation","volume":"114","author":"Liu","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","unstructured":"Richter, S.R., and Roth, S. (2018). Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE."},{"key":"ref_17","first-page":"2525711","article-title":"Applying Deep Learning and Heatmap Techniques for Sinusitis Diagnosis Using Enhanced 3-D Image Segmentation","volume":"74","author":"Huang","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16444","DOI":"10.1109\/ACCESS.2025.3531396","article-title":"Deep Learning-Based Fully Automatic Segmentation of the Paranasal Sinuses in Chronic Rhinosinusitis Patients Using Computed Tomographic Images","volume":"13","author":"Wang","year":"2025","journal-title":"IEEE Access"},{"key":"ref_19","unstructured":"Hamzaoui, I., Benmeziane, H., Cherif, Z., and El Maghraoui, K. (2024). Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-Based Medical Imaging Segmentation. arXiv."},{"key":"ref_20","first-page":"1","article-title":"SSC-Net: A multi-task joint learning network for tongue image segmentation and multi-label classification","volume":"11","author":"Sha","year":"2025","journal-title":"Digit. Health"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103087","DOI":"10.1016\/j.displa.2025.103087","article-title":"DFIT-Net: A novel dynamic feature integration transformer for automatic segmentation of multi-organ structures in medical imaging","volume":"90","author":"Ali","year":"2025","journal-title":"Displays"},{"key":"ref_22","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":"2020","journal-title":"Nat. Methods"},{"key":"ref_23","first-page":"3","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","volume":"Volume 11045","author":"Zhou","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/978-3-031-25066-8_9","article-title":"Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation","volume":"Volume 13803","author":"Cao","year":"2023","journal-title":"Computer Vision\u2014ECCV 2022 Workshops. ECCV 2022"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Yang, D., Roth, H.R., Xu, D., Albarqouni, S., Spampinato, C., Yu, Q., Li, W., Myronenko, A., and Landman, B.A. (2022). UNETR: Transformers for 3D Medical Image Segmentation. Proceedings of the 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TMI.2020.3033541","article-title":"MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction","volume":"40","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1109\/TMI.2021.3078067","article-title":"DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction","volume":"40","author":"Wu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"128109","DOI":"10.1016\/j.eswa.2025.128109","article-title":"COO-DuDo: Computation overhead optimization methods for dual-domain sparse-view CT reconstruction","volume":"286","author":"Deng","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/TMI.2023.3317088","article-title":"MTANet: Multi-Task Attention Network for Automatic Medical Image Segmentation and Classification","volume":"43","author":"Ling","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"120174","DOI":"10.1016\/j.eswa.2023.120174","article-title":"Joint segmentation and classification of skin lesions via a multi-task learning convolutional neural network","volume":"230","author":"He","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102685","DOI":"10.1016\/j.media.2022.102685","article-title":"One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification","volume":"83","author":"Graham","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Marullo, G., Tanzi, L., Ulrich, L., Porpiglia, F., and Vezzetti, E. (2023). A Multi-Task Convolutional Neural Network for Semantic Segmentation and Event Detection in Laparoscopic Surgery. J. Pers. Med., 13.","DOI":"10.3390\/jpm13030413"},{"key":"ref_35","first-page":"108540","article-title":"A multi-task framework for breast cancer segmentation and classification in ultrasound imaging","volume":"260","author":"Remeseiro","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106496","DOI":"10.1016\/j.compbiomed.2022.106496","article-title":"Multi-task deep learning for medical image computing and analysis: A review","volume":"153","author":"Zhao","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dai, G., Dai, D., Wang, C., Tang, Q., Hamilton, M., Chen, H., and Zhang, Y. (2025). Multi-Task Learning Network for Medical Image Analysis Guided by Lesion Regions and Spatial Relationships of Tissues. IEEE Transactions on Circuits and Systems for Video Technology, IEEE.","DOI":"10.1109\/TCSVT.2025.3596803"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103379","DOI":"10.1016\/j.media.2024.103379","article-title":"Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy","volume":"99","author":"Bulpitt","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41746-024-01338-8","article-title":"Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT","volume":"7","author":"Yu","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112881","DOI":"10.1109\/ACCESS.2024.3442380","article-title":"Enhancing Pulmonary Nodule Detection Rate Using 3D Convolutional Neural Networks with Optical Flow Frame Insertion Technique","volume":"12","author":"Zhang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1364\/OE.510398","article-title":"Study on high-precision three-dimensional reconstruction of pulmonary lesions and surrounding blood vessels based on CT images","volume":"32","author":"Chen","year":"2023","journal-title":"Opt. Express"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102867","DOI":"10.1016\/j.media.2023.102867","article-title":"SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations","volume":"88","author":"Pan","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1109\/LSP.2022.3198594","article-title":"SF-Net: A Multi-Task Model for Brain Tumor Segmentation in Multimodal MRI via Image Fusion","volume":"29","author":"Liu","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_44","unstructured":"(2026, March 05). Medical Segmentation Decathlon. Available online: http:\/\/medicaldecathlon.com\/dataaws\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TMI.2004.837793","article-title":"A general tool for the evaluation of spiral CT interpolation algorithms revisiting the effect of pitch in multislice CT","volume":"24","author":"Bricault","year":"2005","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1109\/42.887832","article-title":"Spiral Interpolation Algorithm for Multislice Spiral CT-Part I: Theory","volume":"19","author":"Schaller","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6455","DOI":"10.1118\/1.4966134","article-title":"A comparison of linear interpolation models for iterative CT reconstruction","volume":"43","author":"Hahn","year":"2016","journal-title":"Med. Phys."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1016\/j.ijrobp.2005.11.018","article-title":"Image interpolation in 4D CT using a BSpline deformable registration model","volume":"64","author":"Schreibmann","year":"2006","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1118\/1.2746486","article-title":"A temporal interpolation approach for dynamic reconstruction in perfusion CT","volume":"34","author":"Montesa","year":"2007","journal-title":"Med. Phys."},{"key":"ref_50","first-page":"2669","article-title":"Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising","volume":"37","author":"Marcos","year":"2024","journal-title":"Deleted J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106024","DOI":"10.1016\/j.still.2024.106024","article-title":"APFlowNet: An inter-layer interpolation approach for soil CT images based on CNN and bidirectional optical flow","volume":"238","author":"Bai","year":"2024","journal-title":"Soil Tillage Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Stefaniga, S.A., and Gaianu, M. (2019). An Approach of Segmentation Method Using Deep Learning for CT Medical Images. 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), IEEE.","DOI":"10.1109\/SYNASC49474.2019.00045"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8999","DOI":"10.1002\/mp.17385","article-title":"Attention-enhanced multiscale feature fusion network for pancreas and tumor segmentation","volume":"51","author":"Dong","year":"2024","journal-title":"Med. Phys."},{"key":"ref_54","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"118625","DOI":"10.1016\/j.eswa.2022.118625","article-title":"Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution","volume":"211","author":"Shen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_56","first-page":"1","article-title":"A Multi-Task Framework for Infrared Small Target Detection and Segmentation","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/TMI.2022.3142321","article-title":"A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma Segmentation and IDH Genotyping","volume":"41","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1097\/RLU.0000000000005685","article-title":"Deep Learning\u2013Powered CT-Less Multitracer Organ Segmentation From PET Images","volume":"50","author":"Salimi","year":"2025","journal-title":"Clin. Nucl. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"126807","DOI":"10.1016\/j.eswa.2025.126807","article-title":"Multi-task model of adaptive multi-scale feature fusion and adaptive mixture-of-experts for equipment remaining useful life prediction and fault diagnosis","volume":"272","author":"Zhou","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"108319","DOI":"10.1016\/j.compbiomed.2024.108319","article-title":"Multi-task learning for segmentation and classification of breast tumors from ultrasound images","volume":"173","author":"He","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111726","DOI":"10.1016\/j.asoc.2024.111726","article-title":"Joint learning strategy of multi-scale multi-task convolutional neural network for aero-engine prognosis","volume":"160","author":"Zhou","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_62","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"108837","DOI":"10.1016\/j.bspc.2025.108837","article-title":"D-Net: Dynamic large kernel with dynamic feature fusion for volumetric medical image segmentation","volume":"113","author":"Yang","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_64","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_66","unstructured":"Ruder, S. (2017). An Overview of Multi-Task Learning in Deep Neural Networks. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"103338","DOI":"10.1016\/j.artmed.2025.103338","article-title":"Multi-scale feature enhancement in multi-task learning for medical image analysis","volume":"173","author":"Bui","year":"2026","journal-title":"Artif. Intell. Med."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., and Chi, E.H. (2018). Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery.","DOI":"10.1145\/3219819.3220007"},{"key":"ref_69","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C.Y., and Rabinovich, A. GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. Proceedings of the International Conference on Learning Representations (ICLR), Available online: https:\/\/openreview.net\/forum?id=H1bM1fZCW."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Liu, S., Johns, E.J., and Davison, A.J. (2019). End-To-End Multi-Task Learning with Attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00197"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.1109\/TMI.2024.3394033","article-title":"I3Net: Inter-Intra-Slice Interpolation Network for Medical Slice Synthesis","volume":"43","author":"Song","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7962","DOI":"10.1038\/s41467-024-52260-2","article-title":"Video frame interpolation neural network for 3D tomography across different length scales","volume":"15","author":"Gambini","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"126602","DOI":"10.1016\/j.eswa.2025.126602","article-title":"SFCLI-Net: Spatial-frequency collaborative learning interpolation network for Computed Tomography slice synthesis","volume":"272","author":"Li","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TMI.2025.3589797","article-title":"SegMamba-V2: Long-range Sequential Modeling Mamba For General 3D Medical Image Segmentation","volume":"45","author":"Xing","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3570","DOI":"10.1109\/TMI.2024.3424471","article-title":"HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation","volume":"43","author":"Chen","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"4294","DOI":"10.1109\/TMI.2024.3419707","article-title":"Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning","volume":"43","author":"Wang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"103859","DOI":"10.1016\/j.media.2025.103859","article-title":"SicTTA: Single image continual test time adaptation for medical image segmentation","volume":"108","author":"Wu","year":"2026","journal-title":"Med. Image Anal."}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/6\/80\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T09:52:32Z","timestamp":1780912352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/6\/80"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,30]]},"references-count":77,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["tomography12060080"],"URL":"https:\/\/doi.org\/10.3390\/tomography12060080","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,30]]}}}