Abstract
Unsupervised algorithms are valuable in medicine for discovering hidden patterns without relying on predefined labels. In this work, we conduct an experimental study to test different models on a novel dataset for classifying oocytes as viable or non-viable. The effectiveness of the models is evaluated by measuring the Area Under the Curve (AUC), which is comparable to supervised works. In addition to this, heatmaps have been extracted to provide explainability to the models, so a qualitative analysis has been carried out.
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This study has been partially funded by Agencia Valenciana de la Innovacion (AVI) (INNCAD/2023/124). The work of N. P. García de la Puente was supported by the grant PID2022-140189OB-C21 funded by MICIU/AEI/10.13039/501100011033 ERDF/UE and FSE+. This research was funded an FPU20/03621 (to L.M.) pre-doctoral programme fellowship from the Ministry of Science, Innovation and Universities, Government of Spain. The work by M. López-Pérez was supported by the grant JDC2022-048318-I funded by MICIU/AEI/10.13039/501100011033 the European Union “NextGenerationEU”/PRTR.
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García-de-la-Puente, N.P., Paya, E., Murria, L., López-Pérez, M., Meseguer, M., Naranjo, V. (2025). Unsupervised Prediction of Blastocyst Development from Oocyte Images. In: Juan, A.A., Faulin, J., Lopez-Lopez, D. (eds) Decision Sciences. DSA ISC 2024. Lecture Notes in Computer Science, vol 14779. Springer, Cham. https://doi.org/10.1007/978-3-031-78241-1_19
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DOI: https://doi.org/10.1007/978-3-031-78241-1_19
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