From Image Latent Space to Fuzzy Rules: Interpretable Analysis of Gastrointestinal Foundation Model

arXiv:2610.00414 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Foundation Model — 18× in this paper · explained in 3 episodes
• Latent Space — 15× in this paper · explained in 7 episodes
• Interpretability — 11× in this paper · explained in 15 episodes
• AUC — 9× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Fidelity — 8× in this paper · explained in 2 episodes
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Linear probe — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Clustering — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Proposed Framework — 3× in this paper
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Linear Probing — 2× in this paper · explained in 2 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• International Conference on Computer Vision — 1× in this paper
• Latent Representations — 1× in this paper · explained in 3 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Emerging Properties in Self-Supervised Vision Transformers
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• Post-hoc Part-prototype Networks

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