Benchmarking Vision Transformers for Image Classification in Digital Pathology

  • Valentin ARIOTI

Student thesis: Master typesMaster en sciences informatiques à finalité spécialisée en data science


Experts in digital pathology use artificial intelligence to help them detect disease in images of biological tissue. Convolutional Neural Networks (CNNs) are commonly used for this purpose. In this master thesis, we evaluate a new architecture, Vision Transformer (ViT), by comparing them with CNNs. ViTs have recently appeared in image recognition and have shown promising results. We test their applicability in digital pathology by empirically comparing the two models on a diverse set of biomedical images. The results indicate that both models achieve similar performances, suggesting that they can both be considered as potential choices for disease detection. These findings underline the interest in further exploring the use of ViTs in digital pathology.
la date de réponse19 juin 2023
langue originaleAnglais
L'institution diplômante
  • Universite de Namur
SuperviseurGilles Perrouin (Promoteur), Valentin Delchevalerie (Copromoteur) & Antoine Gratia (Copromoteur)

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