{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T04:28:17Z","timestamp":1761366497689,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry for Digital Transformation and the Civil Service","award":["TSI-100121-2024-35"],"award-info":[{"award-number":["TSI-100121-2024-35"]}]},{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["PID2022-138936OB-C32"],"award-info":[{"award-number":["PID2022-138936OB-C32"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This study introduces a novel Interleaved Fusion Learning (IFL) methodology leveraging transfer learning to generate a family of models optimized for specific datasets while maintaining superior generalization performance across others. The approach is demonstrated in cervical cancer screening, where cytology image datasets present challenges of heterogeneity and imbalance. By interleaving transfer steps across dataset partitions and regulating adaptation through a dynamic learning parameter, IFL promotes both domain-specific accuracy and cross-domain robustness. To evaluate its effectiveness, complementary metrics are used to capture not only predictive accuracy but also fairness in performance distribution across datasets. Results highlight the potential of IFL to deliver reliable and unbiased models in clinical decision support. Beyond cervical cytology, the methodology is designed to be scalable to other medical imaging tasks and, more broadly, to domains requiring equitable AI solutions across multiple heterogeneous datasets.<\/jats:p>","DOI":"10.3390\/make7040128","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:47:36Z","timestamp":1761266856000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interleaved Fusion Learning for Trustworthy AI: Improving Cross-Dataset Performance in Cervical Cancer Analysis"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4648-0297","authenticated-orcid":false,"given":"Carlos","family":"Mart\u00ednez","sequence":"first","affiliation":[{"name":"Cardiology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"},{"name":"AI Platform, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1464-2616","authenticated-orcid":false,"given":"Laura","family":"Busto","sequence":"additional","affiliation":[{"name":"Cardiology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"},{"name":"AI Platform, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3025-8239","authenticated-orcid":false,"given":"Olivia","family":"Zulaica","sequence":"additional","affiliation":[{"name":"Cardiology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"},{"name":"AI Platform, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4165-5049","authenticated-orcid":false,"given":"C\u00e9sar","family":"Veiga","sequence":"additional","affiliation":[{"name":"Cardiology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"},{"name":"AI Platform, Galicia Sur Health Research Institute (IIS Galicia Sur), 36312 Vigo, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"ref_1","first-page":"394","article-title":"Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","unstructured":"World Health Organization (2022). WHO Guidelines for the Use of Thermal Ablation for Cervical Pre-Cancer Lesions, World Health Organization."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s12105-012-0377-0","article-title":"Epidemiology and clinical aspects of HPV in head and neck cancers","volume":"6","author":"Chaturvedi","year":"2012","journal-title":"Head Neck Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1309\/AJCPTGD94EVRSJCG","article-title":"American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer","volume":"137","author":"Saslow","year":"2012","journal-title":"Am. J. Clin. Pathol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.patcog.2018.05.014","article-title":"Deep learning for image-based cancer detection and diagnosis-A survey","volume":"83","author":"Hu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cao, L., Yang, J., Rong, Z., Li, L., Xia, B., You, C., Lou, G., Jiang, L., Du, C., and Meng, H. (2021). A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening. Med. Image Anal., 73.","DOI":"10.1016\/j.media.2021.102197"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-N\u00f3voa, J.A., Busto, L., Campanioni, S., Mart\u00ednez, C., Fari\u00f1a, J., Rodr\u00edguez-Andina, J.J., Juan-Salvadores, P., Jim\u00e9nez, V., \u00cd\u00f1iguez, A., and Veiga, C. (2025). Advancing cuffless arterial blood pressure estimation: A patient-specific optimized approach reducing computational requirements. Future Gener. Comput. Syst., 166.","DOI":"10.1016\/j.future.2024.107689"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.inffus.2020.09.006","article-title":"A survey on deep learning in medicine: Why, how and when?","volume":"66","author":"Piccialli","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kanavati, F., Hirose, N., Ishii, T., Fukuda, A., Ichihara, S., and Tsuneki, M. (2022). A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images. Cancers, 14.","DOI":"10.3390\/cancers14051159"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JBHI.2017.2705583","article-title":"DeepPap: Deep convolutional networks for cervical cell classification","volume":"21","author":"Zhang","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Han, H., Li, M., Wu, X., Yang, H., and Qiao, J. (2025). Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features. Expert Syst. Appl., 260.","DOI":"10.1016\/j.eswa.2024.125445"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, C., Li, M., Li, G., Zhang, Y., Sun, C., and Bai, N. (2022). Cervical Cell\/Clumps Detection in Cytology Images Using Transfer Learning. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102477"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Voiculescu, I. (2023). Dealing with Unreliable Annotations: A Noise-Robust Network for Semantic Segmentation through A Transformer-Improved Encoder and Convolution Decoder. Appl. Sci., 13.","DOI":"10.3390\/app13137966"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Szymoniak, S., Depta, F., Karbowiak, \u0141., and Kubanek, M. (2023). Trustworthy Artificial Intelligence Methods for Users\u2019 Physical and Environmental Security: A Comprehensive Review. Appl. Sci., 13.","DOI":"10.3390\/app132112068"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, F., Wu, P., Ong, H.H., Peterson, J.F., Wei, W.Q., and Zhao, J. (2023). Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction. J. Biomed. Inform., 138.","DOI":"10.1016\/j.jbi.2023.104294"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ferrara, E. (2024). Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci, 6.","DOI":"10.2196\/preprints.48399"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Terzi, D.S., and Azginoglu, N. (2023). In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI. Diagnostics, 13.","DOI":"10.3390\/diagnostics13122110"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Syu, J.H., Fojcik, M., Cupek, R., and Lin, J.C.W. (2025). HTTPS: Heterogeneous Transfer learning for spliT Prediction System evaluated on healthcare data. Inf. Fusion, 113.","DOI":"10.1016\/j.inffus.2024.102617"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, H.E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M.E., and Ganslandt, T. (2022). Transfer learning for medical image classification: A literature review. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00793-7"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Al-Amidie, M., Al-Asadi, A., Humaidi, A.J., Al-Shamma, O., Fadhel, M.A., Zhang, J., Santamar\u00eda, J., and Duan, Y. (2021). Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data. Cancers, 13.","DOI":"10.3390\/cancers13071590"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kupas, D., Hajdu, A., Kovacs, I., Hargitai, Z., Szombathy, Z., and Harangi, B. (2024). Annotated Pap cell images and smear slices for cell classification. Sci. Data, 11.","DOI":"10.1038\/s41597-024-03596-3"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rezende, M.T., Silva, R., Bernardo, F.d.O., Tobias, A.H.G., Oliveira, P.H.C., Machado, T.M., Costa, C.S., Medeiros, F.N.S., Ushizima, D.M., and Carneiro, C.M. (2021). Cric searchable image database as a public platform for conventional pap smear cytology data. Sci. Data, 8.","DOI":"10.1038\/s41597-021-00933-8"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Plissiti, M.E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., and Charchanti, A. (2018, January 7\u201310). Sipakmed: A New Dataset for Feature and Image Based Classification of Normal and Pathological Cervical Cells in Pap Smear Images. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451588"},{"key":"ref_26","unstructured":"Jantzen, J., Norup, J., Dounias, G., and Bjerregaard, B. (2005, January 1). Pap-smear benchmark data for pattern classification. Proceedings of the Nature Inspired Smart Information Systems NiSIS, Albufeira, Portugal."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fang, M., Liao, B., Lei, X., and Wu, F.X. (2024). A systematic review on deep learning based methods for cervical cell image analysis. Neurocomputing, 610.","DOI":"10.1016\/j.neucom.2024.128630"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hussain, E., Mahanta, L.B., Borah, H., and Das, C.R. (2020). Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions. Data Brief, 30.","DOI":"10.1016\/j.dib.2020.105589"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nayar, R., and Wilbur, D.C. (2015). The Bethesda System for Reporting Cervical Cytology: Definitions, Criteria, and Explanatory Notes, Springer.","DOI":"10.1007\/978-3-319-11074-5"},{"key":"ref_30","unstructured":"Ultralytics (2025, August 15). YOLO by Ultralytics. Version 8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_31","unstructured":"Yaseen, M. (2024). What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Weiss, K., Khoshgoftaar, T.M., and Wang, D. (2016). A survey of transfer learning. J. Big Data, 3.","DOI":"10.1186\/s40537-016-0043-6"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T04:24:31Z","timestamp":1761366271000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,23]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040128"],"URL":"https:\/\/doi.org\/10.3390\/make7040128","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2025,10,23]]}}}