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A Multi-Teacher Knowledge Distillation Framework for Enhancing the Robustness of Automated Sperm Morphology Assessment

<b>Background/Objectives:</b> The manual analysis of sperm morphology, crucial for male infertility diagnosis, is subjective and time-consuming. Automated methods using deep learning, offer a promising alternative; however, standard deep models are prone to overfitting when applied to small, heavily...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Osman Emre Tutay, Hamza Osman Ilhan, Hakkı Uzun, Merve Huner Yigit, Gorkem Serbes
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2026-04-01
Schriftenreihe:Diagnostics
Schlagworte:
knowledge distillation
multi teacher learning
sperm morphology classification
class imbalance
infertility
Online-Zugang:https://www.mdpi.com/2075-4418/16/8/1230
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