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Labeled Patches Dataset for Semi-supervised YOLO Training on Cervical Cytology WSI [Dataset]
dc.contributor.authorCardona-Mendoza, Andrés Felipe
dc.contributor.authorGil González, Ana Belén 
dc.contributor.authorPerdomo Lara, Sandra Janneth
dc.contributor.authorRossi Labianca, Lautaro
dc.contributor.authorMarcos Recio, Sandra
dc.contributor.authorBarrero Bueno, Andrés
dc.coverage.spatialColombia – Bogotá D.C. and 11 branches of Liga Colombiana Contra el Cáncer, lat=40.705781, long=-74.015334es_ES
dc.coverage.temporalstart=2023-07 and end=2025-02es_ES
dc.date.accessioned2026-04-07T12:30:25Z
dc.date.available2026-04-07T12:30:25Z
dc.date.issued2026
dc.identifier.citationCardona-Mendoza, A., Gil-González, A.B., et al. (2025). Labeled Patches Dataset for Semi-supervised YOLO Training on Cervical Cytology WSI. GREDOS Repository, University of Salamanca.es_ES
dc.identifier.urihttp://hdl.handle.net/10366/170872
dc.descriptionHere is the English version of the abstract, optimized for international repositories (like Zenodo, IEEE Dataport, or Kaggle) using standard terminology in Digital Pathology and Computer Vision: 📝 Dataset Abstract Title Suggestion: Automated Cervical Cytology Screening: A Labeled WSI Patch Dataset for Deep Learning-Based Object Detection. Abstract: The automated detection of cellular structures using Deep Learning models represents a key strategy to optimize cervical cancer screening by reducing clinical workload and inter-observer variability. However, analyzing Whole Slide Images (WSI) presents critical challenges, including the scarcity of high-quality annotations, high morphological complexity, and significant class imbalance. This dataset addresses these limitations by providing a collection of 640×640px image patches extracted from conventional Papanicolaou (Pap) tests from a Colombian clinical cohort. The data was processed through a semi-automated pipeline integrating manual annotation in QuPath, automated patch extraction, and YOLO label generation via Groovy and Python scripts. To ensure high diagnostic reliability, a web-based expert validation interface was implemented for final label verification. This resource supports the development of robust computer vision models aimed at enhancing precision and efficiency in digital cytological diagnosis.es_ES
dc.description.abstract[EN]This dataset contains labeled image patches extracted from Colombian Whole Slide Images (WSIs) of conventional Papanicolaou (Pap) tests. It supports the training and validation of object detection models (e.g., YOLO) in automated cervical cytology diagnosis. The patches (640×640px JPGs) were extracted and labeled using a semi-automated pipeline combining manual annotation in QuPath and automated patch extraction and YOLO label generation via Groovy and Python scripts. A web-based expert validation interface was used to ensure label accuracy.es_ES
dc.description.sponsorshipThis work was supported by the Secretaría Distrital de Salud de Bogotá (Colombia) and the ATENEA Agency (grant number 368-2022), in collaboration with the Spanish Ministry for Digital Transformation and Civil Service through the University-Enterprise Chair Call (Cátedras ENIA 2022) (Grant TSI-100933-2023-1), co-funded by the European Union NextGenerationEU/PRTR.es_ES
dc.description.tableofcontents1. File List: ✓ JPG patches (640x640) ✓ YOLO-format labels (.txt) ✓ Visual validated patches (.jpg with bounding boxes) ✓ Metadata snapshots (.csv) ✓ Logs of processed annotations (.txt) 2. Relationship between files, if important: Each patch corresponds to a label file. Patches and labels are grouped by class. Visual validated images are derived from the patch-label pair. 3. File format: ✓ Images: .jpg ✓ Labels: .txt (YOLO format) ✓ Logs/Snapshots: .csv, .txtes_ES
dc.language.isoenges_ES
dc.publisherUniversidad de Salamancaes_ES
dc.relation.isreferencedbyAndrés, CM., Ana-Belén, GG., Hortua, H.J., Lautaro, R.L., Sandra, PL. (2026). Prototype of a Comprehensive System for Automated Generation and Expert Validation of Labeled Patches on Papanicolaou Test WSI Images for Semi-supervised Training of YOLO Models in Automated Cervical Cytology Diagnosis. In: Fdez-Riverola, F., et al. Practical Applications of Computational Biology and Bioinformatics, 19th International Conference (PACBB 2025). PACBB2025 2025. Lecture Notes in Networks and Systems, vol 1720. Springer, Cham. https://doi.org/10.1007/978-3-032-10634-6_1es_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.subjectCervical cytologyes_ES
dc.subjectPapanicolaou testes_ES
dc.subjectYOLOes_ES
dc.subjectWhole Slide Imagees_ES
dc.subjectobject detectiones_ES
dc.subjectpatch datasetes_ES
dc.subjectdigital pathologyes_ES
dc.subjectlabel validationes_ES
dc.subject.meshCytodiagnosis *
dc.subject.meshPapanicolaou Test *
dc.subject.meshUterine Cervical Neoplasms *
dc.subject.meshCervix Uteri *
dc.titleLabeled Patches Dataset for Semi-supervised YOLO Training on Cervical Cytology WSI [Dataset]es_ES
dc.typeinfo:eu-repo/semantics/datasetes_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
dc.subject.unesco2407.04 Citologíaes_ES
dc.subject.unesco2209.09 Radiación Infrarrojaes_ES
dc.subject.unesco2209.90 Tratamiento Digital. Imágeneses_ES
dc.identifier.doi10.71636/g7s3-1n94
dc.relation.projectIDgrantAgreement/TSI-100933-2023-1es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.subject.decscuello del útero *
dc.subject.decsneoplasias del cuello uterino *
dc.subject.decscitodiagnóstico *
dc.subject.decsprueba de Papanicolau *
dc.publication.year2026


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