{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:06:03Z","timestamp":1783526763683,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate analysis of medical videos remains a major challenge in deep learning (DL) due to the need for effective spatiotemporal feature mapping that captures both spatial detail and temporal dynamics. Despite advances in DL, most existing models in medical AI focus on static images, overlooking critical temporal cues present in video data. To bridge this gap, a novel DL-based framework is proposed for spatiotemporal feature extraction from medical video sequences. As a feasibility use case, this study focuses on gastrointestinal (GI) endoscopic video classification. A 3D convolutional neural network (CNN) is developed to classify upper and lower GI endoscopic videos using the hyperKvasir dataset, which contains 314 lower and 60 upper GI videos. To address data imbalance, 60 matched pairs of videos are randomly selected across 20 experimental runs. Videos are resized to 224 \u00d7 224, and the 3D CNN captures spatiotemporal information. A 3D version of the parallel spatial and channel squeeze-and-excitation (P-scSE) is implemented, and a new block called the residual with parallel attention (RPA) block is proposed by combining P-scSE3D with a residual block. To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. The model achieves an average accuracy of 0.933, precision of 0.932, recall of 0.944, F1-score of 0.935, and AUC of 0.933. It is also observed that the integration of P-scSE3D increased the F1-score by 7%. This preliminary work opens avenues for exploring various GI endoscopic video-based prospective studies.<\/jats:p>","DOI":"10.3390\/jimaging11070243","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T11:12:34Z","timestamp":1752837154000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel 3D Convolutional Neural Network-Based Deep Learning Model for Spatiotemporal Feature Mapping for Video Analysis: Feasibility Study for Gastrointestinal Endoscopic Video Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9976-0039","authenticated-orcid":false,"given":"Mrinal Kanti","family":"Dhar","sequence":"first","affiliation":[{"name":"Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mou","family":"Deb","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Poonguzhali","family":"Elangovan","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keerthy","family":"Gopalakrishnan","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divyanshi","family":"Sood","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Avneet","family":"Kaur","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charmy","family":"Parikh","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swetha","family":"Rapolu","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-2988","authenticated-orcid":false,"given":"Gianeshwaree Alias Rachna","family":"Panjwani","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rabiah Aslam","family":"Ansari","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naghmeh","family":"Asadimanesh","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiva Sankari","family":"Karuppiah","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7590-2293","authenticated-orcid":false,"given":"Scott A.","family":"Helgeson","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"},{"name":"Department of Critical Care Medicine, Division of Pulmonary Medicine, Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkata S.","family":"Akshintala","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology & Hepatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3251-5415","authenticated-orcid":false,"given":"Shivaram P.","family":"Arunachalam","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"},{"name":"Department of Critical Care Medicine, Division of Pulmonary Medicine, Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1606305","DOI":"10.3389\/ijph.2023.1606305","article-title":"Excess deaths of gastrointestinal, liver, and pancreatic diseases during the COVID-19 pandemic in the United States","volume":"68","author":"Han","year":"2023","journal-title":"Int. J. Public Health"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Adedire, O., Love, N.K., Hughes, H.E., Buchan, I., Vivancos, R., and Elliot, A.J. (2024). Early Detection and Monitoring of Gastrointestinal Infections Using Syndromic Surveillance: A Systematic Review. Int. J. Environ. Res. Public Health, 21.","DOI":"10.3390\/ijerph21040489"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s41597-020-00622-y","article-title":"HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy","volume":"7","author":"Borgli","year":"2020","journal-title":"Sci. Data"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"200","DOI":"10.3949\/ccjm.89a.20061","article-title":"Capsule endoscopy in gastrointestinal disease: Evaluation, diagnosis, and treatment","volume":"89","author":"Akpunonu","year":"2022","journal-title":"Clevel. Clin. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103638","DOI":"10.1016\/j.jbi.2020.103638","article-title":"Residual LSTM layered CNN for classification of gastrointestinal tract diseases","volume":"113","year":"2021","journal-title":"J. Biomed. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1007\/s00371-021-02322-z","article-title":"A systematic review on application of deep learning in digestive system image processing","volume":"39","author":"Zhuang","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"388","DOI":"10.5009\/gnl18384","article-title":"Overview of deep learning in gastrointestinal endoscopy","volume":"13","author":"Min","year":"2019","journal-title":"Gut Liver"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, E.-S., and Lee, K.-S. (2024). Artificial Intelligence in Gastrointestinal Disease: Diagnosis and Management, MDPI-Multidisciplinary Digital Publishing Institute.","DOI":"10.3390\/books978-3-7258-0654-6"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sethi, A., Damani, S., Sethi, A.K., Rajagopal, A., Gopalakrishnan, K., Cherukuri, A.S.S., and Arunachalam, S.P. (2023, January 18\u201320). Gastrointestinal Endoscopic Image Classification using a Novel Wavelet Decomposition Based Deep Learning Algorithm. Proceedings of the 2023 IEEE International Conference on Electro Information Technology (eIT), Romeoville, IL, USA.","DOI":"10.1109\/eIT57321.2023.10187226"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lonseko, Z.M., Adjei, P.E., Du, W., Luo, C., Hu, D., Zhu, L., Gan, T., and Rao, N. (2021). Gastrointestinal disease classification in endoscopic images using attention-guided convolutional neural networks. Appl. Sci., 11.","DOI":"10.3390\/app112311136"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Owais, M., Arsalan, M., Choi, J., Mahmood, T., and Park, K.R. (2019). Artificial intelligence-based classification of multiple gastrointestinal diseases using endoscopy videos for clinical diagnosis. J. Clin. Med., 8.","DOI":"10.3390\/jcm8070986"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"139489","DOI":"10.1109\/ACCESS.2021.3118541","article-title":"Video processing using deep learning techniques: A systematic literature review","volume":"9","author":"Sharma","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yu, T., Hu, H., Zhang, X., Lei, H., Liu, J., Hu, W., Duan, H., and Si, J. (2022). Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos. Appl. Sci., 12.","DOI":"10.3390\/app12073306"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., and Paluri, M. (2018, January 18\u201323). A closer look at spatiotemporal convolutions for action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., and Mei, T. (2017, January 22\u201329). Learning spatio-temporal representation with pseudo-3d residual networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.590"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Scovanner, P., Ali, S., and Shah, M. (2007, January 24\u201329). A 3-dimensional sift descriptor and its application to action recognition. Proceedings of the 15th ACM International Conference on Multimedia, Augsburg, Germany.","DOI":"10.1145\/1291233.1291311"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Klaser, A., Marsza\u0142ek, M., and Schmid, C. (2008, January 1\u20134). A spatio-temporal descriptor based on 3d-gradients. Proceedings of the BMVC 2008-19th British Machine Vision Conference, Leeds, UK.","DOI":"10.5244\/C.22.99"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Willems, G., Tuytelaars, T., and Van Gool, L. An efficient dense and scale-invariant spatio-temporal interest point detector. Computer Vision\u2013ECCV 2008, Proceedings of the 10th European Conference on Computer Vision, Marseille, France, 12\u201318 October 2008, Proceedings, Part II 10, Springer.","DOI":"10.1007\/978-3-540-88688-4_48"},{"key":"ref_19","first-page":"3200","article-title":"Human action recognition from various data modalities: A review","volume":"45","author":"Sun","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_21","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ibrahim, M.S., Muralidharan, S., Deng, Z., Vahdat, A., and Mori, G. (2016, January 27\u201330). A hierarchical deep temporal model for group activity recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.217"},{"key":"ref_23","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.neucom.2020.05.118","article-title":"DB-LSTM: Densely-connected Bi-directional LSTM for human action recognition","volume":"444","author":"He","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_25","unstructured":"Ballas, N., Yao, L., and Pal, C. (2015). Delving deeper into convolutional networks for learning video representations. arXiv."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, L., Qiao, Y., and Tang, X. (2015, January 7\u201312). Action recognition with trajectory-pooled deep-convolutional descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Duta, I.C., Nguyen, T.A., Aizawa, K., Ionescu, B., and Sebe, N. (2016, January 4\u20138). Boosting VLAD with double assignment using deep features for action recognition in videos. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899964"},{"key":"ref_29","unstructured":"Ali, D., Mohsen, F., Vivek, S., Amir, H.K., Mohammad, M.A., Rahman, Y., and Luc, V.G. (2017). Temporal 3d convnets: New architecture and transfer learning for video classification. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Carreira, J., and Zisserman, A. (2017, January 21\u201326). Quo vadis, action recognition? A new model and the kinetics dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1109\/JBHI.2021.3099816","article-title":"Assessment of Parkinson\u2019s disease severity from videos using deep architectures","volume":"26","author":"Yin","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, H., Ho, E.S.L., Zhang, F.X., and Shum, H.P.H. (2022, January 18\u201322). Pose-based tremor classification for Parkinson\u2019s disease diagnosis from video. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Singapore.","DOI":"10.1007\/978-3-031-16440-8_47"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, G.Y., Chen, L., Zahiri, M., Balaraju, N., Patil, S., Mehanian, C., Gregory, C., Gregory, K., Raju, B., and Kruecker, J. (2023, January 2\u20136). Weakly Semi-Supervised Detector-Based Video Classification with Temporal Context for Lung Ultrasound. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCVW60793.2023.00262"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shea, D.E., Kulhare, S., Millin, R., Laverriere, Z., Mehanian, C., Delahunt, C.B., Banik, D., Zheng, X., Zhu, M., and Ji, Y. (2023, January 17\u201324). Deep learning video classification of lung ultrasound features associated with pneumonia. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00312"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5547","DOI":"10.1109\/TII.2021.3133307","article-title":"EEGWaveNet: Multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection","volume":"18","author":"Thuwajit","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"S69","DOI":"10.4103\/2468-8827.330653","article-title":"A novel machine learning-based video classification approach to detect pneumonia in COVID-19 patients using lung ultrasound","volume":"6","author":"Krishnaswamy","year":"2021","journal-title":"Int. J. Noncommun. Dis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101572","DOI":"10.1016\/j.media.2019.101572","article-title":"Multi-task recurrent convolutional network with correlation loss for surgical video analysis","volume":"59","author":"Jin","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.media.2006.10.003","article-title":"Informative frame classification for endoscopy video","volume":"11","author":"Oh","year":"2007","journal-title":"Med. Image Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1049\/htl.2019.0066","article-title":"Upper gastrointestinal anatomy detection with multi-task convolutional neural networks","volume":"6","author":"Xu","year":"2019","journal-title":"Health Technol. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lee, J., Oh, J., Shah, S.K., Yuan, X., and Tang, S.J. (2007, January 11\u201315). Automatic classification of digestive organs in wireless capsule endoscopy videos. Proceedings of the 2007 ACM Symposium on Applied Computing, Seoul, Republic of Korea.","DOI":"10.1145\/1244002.1244230"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9545920","DOI":"10.1155\/2017\/9545920","article-title":"An automatic gastrointestinal polyp detection system in video endoscopy using fusion of color wavelet and convolutional neural network features","volume":"2017","author":"Billah","year":"2017","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_42","unstructured":"(2024, April 28). TensorFlow, Video Classification. Available online: https:\/\/www.tensorflow.org\/tutorials\/video\/video_classification."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"163054","DOI":"10.1109\/ACCESS.2021.3132916","article-title":"Diverse temporal aggregation and depthwise spatiotemporal factorization for efficient video classification","volume":"9","author":"Lee","year":"2021","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_45","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_46","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"106057","DOI":"10.1016\/j.bspc.2024.106057","article-title":"FUSegNet: A deep convolutional neural network for foot ulcer segmentation","volume":"92","author":"Dhar","year":"2024","journal-title":"Biomed. Signal Process Control"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TMI.2018.2867261","article-title":"Recalibrating Fully Convolutional Networks with Spatial and Channel \u2018Squeeze and Excitation\u2019 Blocks","volume":"38","author":"Roy","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_50","unstructured":"Dhar, M.K., Wang, C., Patel, Y., Zhang, T., Niezgoda, J., Gopalakrishnan, S., Chen, K., and Yu, Z. (2024). Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study. arXiv."},{"key":"ref_51","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2013Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/7\/243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:12:03Z","timestamp":1760033523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/7\/243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":52,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["jimaging11070243"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11070243","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,18]]}}}