TAP-Path: Task-Adaptive Structural and Token Pruning for Efficient and Trustworthy Pathology Foundation Models

arXiv:2609.04071 · cs.CV, cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-03

Updated: 2026-09-03

Terminology

• Accuracy — 101× in this paper · explained in 1 episode
• Foundation Model — 53× in this paper · explained in 3 episodes
• Pruning — 45× in this paper · explained in 4 episodes
• Foundation Models — 30× in this paper · explained in 7 episodes
• Transformer — 28× in this paper · explained in 2 episodes
• Adaptation — 23× in this paper · explained in 1 episode
• Calibration — 18× in this paper · explained in 7 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Distillation — 12× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• External Validation — 8× in this paper · explained in 2 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Redundancy — 7× in this paper · explained in 2 episodes
• Knowledge Distillation — 5× in this paper · explained in 11 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Cancer Genome Atlas — 3× in this paper
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Clinical Proteomic Tumor Analysis Consortium — 2× in this paper
• Sparsity — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstention — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Hibou: A Family of Foundational Vision Transformers for Pathology
• PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks
• AdaptViG: Adaptive Vision GNN with Exponential Decay Gating

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