{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T13:40:06Z","timestamp":1792590006080,"version":"4.5.0"},"reference-count":43,"publisher":"MyJove Corporation","issue":"231","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JoVE"],"DOI":"10.3791\/70231","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:00:37Z","timestamp":1778281237000},"source":"Crossref","is-referenced-by-count":0,"title":["A Few-shot Multimodal Deep Learning Framework For Precision Diagnosis Of Parotid Gland"],"prefix":"10.3791","author":[{"given":"Yang","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyao","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiguo","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongjian","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhai","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2098","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"issue":"2","key":"ref1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1002\/lary.30154","type":"journal-article","article-title":"A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences","volume":"133","author":"Liu","year":"2023","unstructured":"Liu, X., Pan, Y., Zhang, X., Sha, Y., Wang, S., Li, H., Liu, J. A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences. Laryngoscope. 133(2), 327\u2013335 (2023).","journal-title":"Laryngoscope"},{"issue":"1","key":"ref2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1177\/0194599820932512","type":"journal-article","article-title":"Controversies in the Workup and Surgical Management of Parotid Neoplasms","volume":"164","author":"Moore","year":"2021","unstructured":"Moore, M. G., Yueh, B., Lin, D. T., Bradford, C. R., Smith, R. V., Khariwala, S. S. Controversies in the Workup and Surgical Management of Parotid Neoplasms. Otolaryngol Head Neck Surg. 164(1), 27\u201336 (2021).","journal-title":"Otolaryngol Head Neck Surg"},{"issue":"3","key":"ref3","doi-asserted-by":"crossref","first-page":"438","DOI":"10.4103\/njms.njms_111_22","type":"journal-article","article-title":"Clinico-Pathological Profile of Parotid Gland Tumors at a Tertiary Care Center in North India","volume":"14","author":"Gautam","year":"2023","unstructured":"Gautam, S. K., Kumar, S., Singh, H. P., Singh, A. B., Chandra, M. Clinico-Pathological Profile of Parotid Gland Tumors at a Tertiary Care Center in North India. Natl J Maxillofac Surg. 14(3), 438\u2013443 (2023).","journal-title":"Natl J Maxillofac Surg"},{"issue":"4","key":"ref4","doi-asserted-by":"crossref","first-page":"rjac039","DOI":"10.1093\/jscr\/rjac039","type":"journal-article","article-title":"Oncocytic Mucoepidermoid Carcinoma of the Parotid Gland: Case Report","volume":"2022","author":"Alshammari","year":"2022","unstructured":"Alshammari, A., Aljufairi, E., Alsayed, A. Oncocytic Mucoepidermoid Carcinoma of the Parotid Gland: Case Report. J Surg Case Rep. 2022(4), rjac039 (2022).","journal-title":"J Surg Case Rep"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3389\/fsurg.2018.00039","type":"journal-article","article-title":"Contemporary Management of Benign and Malignant Parotid Tumors","volume":"5","author":"Thielker","year":"2018","unstructured":"Thielker, J., Grosheva, M., Ihrler, S., Wittig, A., Guntinas-Lichius, O. Contemporary Management of Benign and Malignant Parotid Tumors. Front Surg. 5, 39 (2018).","journal-title":"Front Surg"},{"issue":"4","key":"ref6","first-page":"246","type":"journal-article","article-title":"The Efficacy of Magnetic Resonance Imaging and Color Doppler Ultrasonography in Diagnosis of Salivary Gland Tumors","volume":"8","author":"Davachi","year":"2014","unstructured":"Davachi, B., Imanimoghaddam, M., Majidi, M. R., Sahebalam, A., Johari, M., Langaroodi, A. J., Shakeri, M. T. The Efficacy of Magnetic Resonance Imaging and Color Doppler Ultrasonography in Diagnosis of Salivary Gland Tumors. J Dent Res Dent Clin Dent Prospects. 8(4), 246\u2013250 (2014).","journal-title":"J Dent Res Dent Clin Dent Prospects"},{"issue":"4","key":"ref7","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.3390\/curroncol31040151","type":"journal-article","article-title":"Preoperative Direct Puncture Embolization of Castleman Disease of the Parotid Gland: A Case Report","volume":"31","author":"Pedicelli","year":"2024","unstructured":"Pedicelli, A., Trombatore, P., Bartolo, A., Camilli, A., Rossi, E. D., Scarcia, L., Alexandre, A. M. Preoperative Direct Puncture Embolization of Castleman Disease of the Parotid Gland: A Case Report. Curr Oncol. 31(4), 2047\u20132056 (2024).","journal-title":"Curr Oncol"},{"issue":"1","key":"ref8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s00405-003-0730-8","type":"journal-article","article-title":"Preoperative Diagnostic Values of Fine-Needle Cytology and MRI in Parotid Gland Tumors","volume":"262","author":"Paris","year":"2005","unstructured":"Paris, J., Facon, F., Pascal, T., Chrestian, M., Moulin, G., Zanaret, M. Preoperative Diagnostic Values of Fine-Needle Cytology and MRI in Parotid Gland Tumors. Eur Arch Otorhinolaryngol. 262(1), 27\u201331 (2005).","journal-title":"Eur Arch Otorhinolaryngol"},{"issue":"12","key":"ref9","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.15537\/smj.2016.12.16288","type":"journal-article","article-title":"Diffusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Parotid Masses: Preliminary Results","volume":"37","author":"Yologlu","year":"2016","unstructured":"Yologlu, Z., Aydin, H., Alp, N. A., Aribas, B. K., Kizilgoz, V., Arda, K. Diffusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Parotid Masses: Preliminary Results. Saudi Med J. 37(12), 1412\u20131417 (2016).","journal-title":"Saudi Med J"},{"issue":"1","key":"ref10","first-page":"19","type":"journal-article","article-title":"Cross-Sectional Imaging and Cytologic Investigations in the Preoperative Diagnosis of Parotid Gland Tumors: An Updated Literature Review","volume":"21","author":"Stoia","year":"2021","unstructured":"Stoia, S. et al. Cross-Sectional Imaging and Cytologic Investigations in the Preoperative Diagnosis of Parotid Gland Tumors: An Updated Literature Review. Bosn J Basic Med Sci. 21(1), 19\u201332 (2021).","journal-title":"Bosn J Basic Med Sci"},{"issue":"7","key":"ref11","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.3174\/ajnr.A2520","type":"journal-article","article-title":"MR Imaging of Parotid Tumors: Typical Lesion Characteristics Improve Discrimination Between Benign and Malignant Disease","volume":"32","author":"Christe","year":"2011","unstructured":"Christe, A., Waldherr, C., Hallett, R., Zbaeren, P., Thoeny, H. MR Imaging of Parotid Tumors: Typical Lesion Characteristics Improve Discrimination Between Benign and Malignant Disease. Am J Neuroradiol. 32(7), 1202\u20131207 (2011).","journal-title":"Am J Neuroradiol"},{"issue":"6","key":"ref12","doi-asserted-by":"crossref","first-page":"588","DOI":"10.21037\/qims.2018.07.07","type":"journal-article","article-title":"Diagnostic Value of Single-Source Dual-Energy Spectral Computed Tomography in Differentiating Parotid Gland Tumors: Initial Results","volume":"8","author":"Li","year":"2018","unstructured":"Li, L., Zhao, Y., Luo, D., Yang, L., Hu, L., Zhao, X., Wang, Y., Liu, W. Diagnostic Value of Single-Source Dual-Energy Spectral Computed Tomography in Differentiating Parotid Gland Tumors: Initial Results. Quant Imaging Med Surg. 8(6), 588\u2013596 (2018).","journal-title":"Quant Imaging Med Surg"},{"issue":"2","key":"ref13","doi-asserted-by":"crossref","first-page":"e187","DOI":"10.1093\/oncolo\/oyad227","type":"journal-article","article-title":"Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment","volume":"29","author":"Gu","year":"2024","unstructured":"Gu, J., Zhong, X., Fang, C., Lou, W., Fu, P., Woodruff, H. C., Wang, B., Jiang, T., Lambin, P. Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment. Oncologist. 29(2), e187\u2013e197 (2024).","journal-title":"Oncologist"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1402967","DOI":"10.3389\/fmed.2024.1402967","type":"journal-article","article-title":"Deep Learning Radiomics Based on Multimodal Imaging for Distinguishing Benign and Malignant Breast Tumors","volume":"11","author":"Lu","year":"2024","unstructured":"Lu, G., Tian, R., Yang, W., Liu, R., Liu, D., Xiang, Z., Zhang, G. Deep Learning Radiomics Based on Multimodal Imaging for Distinguishing Benign and Malignant Breast Tumors. Front Med. 11, 1402967 (2024).","journal-title":"Front Med"},{"issue":"14","key":"ref15","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.3390\/diagnostics14171871","type":"journal-article","article-title":"Advancing Prostate Cancer Diagnosis: A Deep Learning Approach for Enhanced Detection in MRI Images","volume":"14","author":"Horasan","year":"2024","unstructured":"Horasan, A., G\u00fcne\u015f, A. Advancing Prostate Cancer Diagnosis: A Deep Learning Approach for Enhanced Detection in MRI Images. Diagnostics. 14(14), 1871 (2024).","journal-title":"Diagnostics"},{"issue":"5","key":"ref16","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.21037\/qims-22-950","type":"journal-article","article-title":"A Deep Learning Model for the Differential Diagnosis of Benign and Malignant Salivary Gland Tumors Based on Ultrasound Imaging and Clinical Data","volume":"13","author":"Zhang","year":"2023","unstructured":"Zhang, G. et al. A Deep Learning Model for the Differential Diagnosis of Benign and Malignant Salivary Gland Tumors Based on Ultrasound Imaging and Clinical Data. Quant Imaging Med Surg. 13(5), 2989\u20133000 (2023).","journal-title":"Quant Imaging Med Surg"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"919088","DOI":"10.3389\/fonc.2022.919088","type":"journal-article","article-title":"Using Deep Learning to Distinguish Malignant from Benign Parotid Tumors on Plain Computed Tomography Images","volume":"12","author":"Hu","year":"2022","unstructured":"Hu, Z., Wang, B., Pan, X., Cao, D., Gao, A., Yang, X., Chen, Y., Lin, Z. Using Deep Learning to Distinguish Malignant from Benign Parotid Tumors on Plain Computed Tomography Images. Front Oncol. 12, 919088 (2022).","journal-title":"Front Oncol"},{"issue":"1","key":"ref18","doi-asserted-by":"crossref","first-page":"9945","DOI":"10.1038\/s41598-024-60200-9","type":"journal-article","article-title":"MRI-Based Radiomics for Predicting Histology in Malignant Salivary Gland Tumors: Methodology and Proof of Principle","volume":"14","author":"Khodabakhshi","year":"2024","unstructured":"Khodabakhshi, Z. et al. MRI-Based Radiomics for Predicting Histology in Malignant Salivary Gland Tumors: Methodology and Proof of Principle. Sci Rep. 14(1), 9945 (2024).","journal-title":"Sci Rep"},{"issue":"2","key":"ref19","first-page":"402","type":"journal-article","article-title":"Multiparametric MRI in Diagnosis of Parotid Gland Tumor: An Observational Study in 3-T MRI","volume":"35","author":"Sreecharan","year":"2024","unstructured":"Sreecharan, V. et al. Multiparametric MRI in Diagnosis of Parotid Gland Tumor: An Observational Study in 3-T MRI. Indian J Radiol Imaging. 35(2), 402\u2013410 (2024).","journal-title":"Indian J Radiol Imaging"},{"issue":"8","key":"ref20","doi-asserted-by":"crossref","first-page":"e4408","DOI":"10.1002\/nbm.4408","type":"journal-article","article-title":"Classification of Parotid Gland Tumors by Using Multimodal MRI and Deep Learning","volume":"34","author":"Chang","year":"2021","unstructured":"Chang, Y. J., Huang, T. Y., Liu, Y. J., Chung, H. W., Juan, C. J. Classification of Parotid Gland Tumors by Using Multimodal MRI and Deep Learning. NMR Biomed. 34(8), e4408 (2021).","journal-title":"NMR Biomed"},{"key":"ref21","type":"journal-article","article-title":"Learning from Few Examples: A Summary of Approaches to Few-Shot Learning","volume":"abs\/2203.04291","author":"Parnami","year":"2022","unstructured":"Parnami, A., Lee, M. Learning from Few Examples: A Summary of Approaches to Few-Shot Learning. ArXiv. abs\/2203.04291 (2022).","journal-title":"ArXiv"},{"key":"ref22","article-title":"A Non-Local Algorithm for Image Denoising","author":"Buades","year":"2005","unstructured":"Buades, A., Coll, B., Morel, J. M. A Non-Local Algorithm for Image Denoising. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 2, 60-65 (2005)."},{"key":"ref23","article-title":"A Review: Data Pre-Processing and Data Augmentation Techniques","author":"Maharana","year":"2022","unstructured":"Maharana, K., Mondal, S., Nemade, B. A Review: Data Pre-Processing and Data Augmentation Techniques. Global Transitions Proceedings. 3, 91-99 (2022)."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"100258","DOI":"10.1016\/j.array.2022.100258","type":"journal-article","article-title":"Data augmentation: A comprehensive survey of modern approaches","volume":"16","author":"Mumuni","year":"2022","unstructured":"Mumuni, A., Mumuni, F. Data augmentation: A comprehensive survey of modern approaches. Array. 16, 100258 (2022).","journal-title":"Array"},{"issue":"24","key":"ref25","doi-asserted-by":"crossref","first-page":"245015","DOI":"10.1088\/1361-6560\/ac3eac","type":"journal-article","article-title":"Impact of Image Pre-Processing Methods on Computed Tomography Radiomics Features in Chronic Obstructive Pulmonary Disease","volume":"66","author":"Au","year":"2021","unstructured":"Au, R. C., Tan, W. C., Bourbeau, J., Hogg, J. C., Kirby, M. Impact of Image Pre-Processing Methods on Computed Tomography Radiomics Features in Chronic Obstructive Pulmonary Disease. Phys Med Biol. 66(24), 245015 (2021).","journal-title":"Phys Med Biol"},{"key":"ref26","article-title":"A Closer Look at Spatiotemporal Convolutions for Action Recognition","author":"Tran","year":"2017","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M. A Closer Look at Spatiotemporal Convolutions for Action Recognition. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6450-6459 (2017)."},{"key":"ref27","article-title":"Deep Residual Learning for Image Recognition","author":"He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770-778 (2016)."},{"key":"ref28","article-title":"Densely Connected Convolutional Networks","author":"Huang","year":"2017","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2261-2269 (2017)."},{"key":"ref29","article-title":"Aggregated Residual Transformations for Deep Neural Networks","author":"Xie","year":"2017","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K. Aggregated Residual Transformations for Deep Neural Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5987-5995 (2017)."},{"key":"ref30","article-title":"Prototypical Networks for Few-Shot Learning","author":"Snell","year":"2017","unstructured":"Snell, J., Swersky, K., Zemel, R. Prototypical Networks for Few-Shot Learning. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing System. 4080 \u2013 4090 (2017)."},{"key":"ref31","article-title":"Revisiting Prototypical Network for Cross-Domain Few-Shot Learning","author":"Zhou","year":"2023","unstructured":"Zhou, F., Wang, P., Zhang, L., Wei, W., Zhang, Y. Revisiting Prototypical Network for Cross-Domain Few-Shot Learning. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 20061-20070 (2023)."},{"key":"ref32","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/TNSRE.2024.3380357","type":"journal-article","article-title":"Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study","volume":"32","author":"Li","year":"2024","unstructured":"Li, T. et al. Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32, 1566-1576 (2024).","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"102718","DOI":"10.1016\/j.artmed.2023.102718","type":"journal-article","article-title":"A Few-Shot Disease Diagnosis Decision Making Model Based on Meta-Learning for General Practice","volume":"147","author":"Liu","year":"2024","unstructured":"Liu, Q., Tian, Y., Zhou, T. et al. A Few-Shot Disease Diagnosis Decision Making Model Based on Meta-Learning for General Practice. Artif Intell Med. 147, 102718 (2024).","journal-title":"Artif Intell Med"},{"key":"ref34","article-title":"Multi-Modal Contrastive Learning for Medical Image Classification with Limited Training Data","author":"Jiao","year":"2024","unstructured":"Jiao, S. et al. Multi-Modal Contrastive Learning for Medical Image Classification with Limited Training Data. 2024 International Conference on Machine Learning and Applications (ICMLA). 1083\u20131088 (2024)."},{"key":"ref35","doi-asserted-by":"crossref","first-page":"105337","DOI":"10.1016\/j.imavis.2024.105337","type":"journal-article","article-title":"ProtoMed: Prototypical Networks with Auxiliary Regularization for Few-Shot Medical Image Classification","volume":"154","author":"Ouahab","year":"2025","unstructured":"Ouahab, A., Ahmed, O. B. ProtoMed: Prototypical Networks with Auxiliary Regularization for Few-Shot Medical Image Classification. Image and Vision Computing. 154, 105337 (2025).","journal-title":"Image and Vision Computing"},{"key":"ref36","type":"journal-article","article-title":"Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis","author":"Wang","year":"2025","unstructured":"Wang, L., Uddin, I. I., Santosh, K. C. Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis. IEEE Journal of Biomedical and Health Informatics. (2025).","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"ref37","article-title":"Squeeze-and-Excitation Networks","author":"Hu","year":"2018","unstructured":"Hu, J., Shen, L., Sun, G. Squeeze-and-Excitation Networks. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 7132\u20137141 (2018)."},{"key":"ref38","article-title":"CBAM: Convolutional Block Attention Module","author":"Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J. Y., Kweon, I. S. CBAM: Convolutional Block Attention Module. Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part VII. 3-19 (2018)."},{"key":"ref39","article-title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","author":"Wang","year":"2020","unstructured":"Wang, Q. et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11534\u201311542 (2020)."},{"key":"ref40","article-title":"Rotate to Attend: Convolutional Triplet Attention Module","author":"Misra","year":"2021","unstructured":"Misra, D. et al. Rotate to Attend: Convolutional Triplet Attention Module. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). 3138\u20133147 (2021)."},{"key":"ref41","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","author":"Selvaraju","year":"2017","unstructured":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision (ICCV). 618-626 (2017)."},{"issue":"2","key":"ref42","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.32604\/cmc.2023.041419","type":"journal-article","article-title":"Grad-CAM: Understanding AI Models","volume":"76","author":"Wang","year":"2023","unstructured":"Wang, S., Zhang, Y. Grad-CAM: Understanding AI Models. Computers, Materials & Continua. 76(2), 1321\u20131324 (2023).","journal-title":"Computers, Materials & Continua"},{"key":"ref43","article-title":"A Unified Approach to Interpreting Model Predictions","author":"Lundberg","year":"2017","unstructured":"Lundberg, S. M., Lee, S. I. A Unified Approach to Interpreting Model Predictions. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems. 4768 \u2013 4777 (2017)."}],"container-title":["Journal of Visualized Experiments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jove.com\/t\/70231\/a-few-shot-multimodal-deep-learning-framework-for-precision-diagnosis","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T12:51:44Z","timestamp":1792587104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jove.com\/t\/70231\/a-few-shot-multimodal-deep-learning-framework-for-precision-diagnosis"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":43,"journal-issue":{"issue":"231","published-online":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.3791\/70231","relation":{},"ISSN":["1940-087X"],"issn-type":[{"value":"1940-087X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]}}}