Comparative classification performance using VGG, ResNet, and ViT models, with each model applied in two different training approaches on segmented cells: trained from scratch and fine-tuned.

Comparative classification performance using VGG, ResNet, and ViT models, with each model applied in two different training approaches on segmented cells: trained from scratch and fine-tuned.

<p>Comparative classification performance using VGG, ResNet, and ViT models, with each model applied in two different training approaches on segmented cells: trained from scratch and fine-tuned.</p>

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Bibliographic Details
Main Author:	Oriana Presacan (22232246) (author)
Other Authors:	María Hernández Mesa (12800697) (author), Alexandru C. Aldea (22232249) (author), Siri Andresen (22232252) (author), Amani Al Outa (22232255) (author), Julie Aarmo Johannessen (22232258) (author), Bogdan Ionescu (21020384) (author), Helene Knævelsrud (3604124) (author), Michael A. Riegler (12488153) (author)
Published:	2025
Subjects:	Cell Biology Genetics Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> autophagy streamline biomedical research numerous pathological conditions handling complex scenarios custom algorithm capable complexity pose challenges class activation mappings biology experts validated analyze model decision advance autophagy research reduce manual effort pipeline &# 8217 maintaining cellular homeostasis validate classification outcomes computational pipeline cellular tracking cellular dataset vision transformer track cells sne visualizations object detection neurodegenerative diseases net ++ morphological changes manual analysis making processes including cancer explainable ai explainability methods enhance transparency dynamic nature deeper insights deep learning damaged components cell division autophagic state autophagic processes
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