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Analysis of institutional authors

Hernandez-Garcia, SergioCorresponding AuthorCuesta-Infante, AlfredoAuthorMontemayor, Antonio SAuthor

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Real-like synthetic sperm video generation from learned behaviors

Publicated to:Applied Intelligence. 55 (6): 518- - 2025-04-01 55(6), DOI: 10.1007/s10489-025-06407-3

Authors: Hernandez-Garcia, Sergio; Cuesta-Infante, Alfredo; Makris, Dimitrios; Montemayor, Antonio S

Affiliations

Kingston Univ, London, England - Author
Univ Rey Juan Carlos, Mostoles, Spain - Author

Abstract

Computer-assisted sperm analysis is an open research problem, and a main challenge is how to test its performance. Deep learning techniques have boosted computer vision tasks to human-level accuracy, when sufficiently large labeled datasets were provided. However, when it comes to sperm (either human or not) there is lack of sufficient large datasets for training and testing deep learning systems. In this paper we propose a solution that provides access to countless fully annotated and realistic synthetic video sequences of sperm. Specifically, we introduce a parametric model of a spermatozoon, which is animated along a video sequence using a denoising diffusion probabilistic model. The resulting videos are then rendered with a photo-realistic appearance via a style transfer procedure using a CycleGAN. We validate our synthetic dataset by training a deep object detection model on it, achieving state-of-the-art performance once validated on real data. Additionally, an evaluation of the generated sequences revealed that the behavior of the synthetically generated spermatozoa closely resembles that of real ones.

Keywords

Diffusion modelsSperm analysisSperm modelingStyle transfeStyle transferSynthetic datasetSynthetic video

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied Intelligence due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 78/197, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Artificial Intelligence. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría .

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-16:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 3 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Hernández García, Sergio) and Last Author (Sanz Montemayor, Antonio).

the author responsible for correspondence tasks has been Hernández García, Sergio.