Temporal progression of a starved cell with explainable AI heatmaps.

Temporal progression of a starved cell with explainable AI heatmaps.

<p>The figure shows a five-time-point sequence from the test dataset, accompanied by GradCAM, EigenCAM, and AblationCAM heatmaps. These highlight key image regions influencing the ViT model’s predictions, with the predicted autophagy status shown above each frame. Notably, an error occurs at t...

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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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