<abstract xmlns="http://www.w3.org/1999/xhtml">

Colposcopy imaging is pivotal in cervical cancer diagnosis, a major health concern for women. The computational challenge lies in accurate lesion recognition. A significant hindrance for many existing machine learning solutions is the scarcity of comprehensive training datasets.
To reduce this gap, we present AnnoCerv: a comprehensive dataset tailored for feature-driven and image-based colposcopy analysis. Distinctively, AnnoCerv include detailed segmentations, expert-backed colposcopic annotations and Swede scores, and a wide image variety including acetic acid, iodine, and green-filtered captures. This rich dataset supports the training of models for classifying and segmenting low-grade squamous intraepithelial lesions, detecting high-grade lesions, aiding colposcopy-guided biopsies, and predicting Swede scores – a crucial metric for medical assessments and treatment strategies.
To further assist researchers, our release includes code that demonstrates data handling and processing and exemplifies a simple feature extraction and classification technique.
</abstract>

Colposcopy imaging is pivotal in cervical cancer diagnosis, a major health concern for women. The computational challenge lies in accurate lesion recognition. A significant hindrance for many existing machine learning solutions is the scarcity of comprehensive training datasets.
To reduce this gap, we present AnnoCerv: a comprehensive dataset tailored for feature-driven and image-based colposcopy analysis. Distinctively, AnnoCerv include detailed segmentations, expert-backed colposcopic annotations and Swede scores, and a wide image variety including acetic acid, iodine, and green-filtered captures. This rich dataset supports the training of models for classifying and segmenting low-grade squamous intraepithelial lesions, detecting high-grade lesions, aiding colposcopy-guided biopsies, and predicting Swede scores – a crucial metric for medical assessments and treatment strategies.
To further assist researchers, our release includes code that demonstrates data handling and processing and exemplifies a simple feature extraction and classification technique.

AnnoCerv: A new dataset for feature-driven and image-based automated colposcopy analysis

University of Medicine and Pharmacy Iaşi

Sapientia Hungarian University of Transylvania

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Colposcopy imaging is pivotal in cervical cancer diagnosis, a major health concern for women. The computational challenge lies in accurate lesion recognition....

AnnoCerv: A new dataset for feature-driven and image-based automated colposcopy analysis

Data publikacji: 12 gru 2023

Zakres stron: 306 - 329

Otrzymano: 18 sie 2023

DOI: https://doi.org/10.2478/ausi-2023-0019

Słowa kluczowe
colposcopy imaging, cervical cancer diagnosis, lesion segmentation, automated image analysis

© 2023 Dorina Adelina Minciună et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

AnnoCerv: A new dataset for feature-driven and image-based automated colposcopy analysis

Data publikacji: 12 gru 2023

Zakres stron: 306 - 329

Otrzymano: 18 sie 2023

DOI: https://doi.org/10.2478/ausi-2023-0019

Słowa kluczowecolposcopy imaging, cervical cancer diagnosis, lesion segmentation, automated image analysis

© 2023 Dorina Adelina Minciună et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
colposcopy imaging, cervical cancer diagnosis, lesion segmentation, automated image analysis