Architecture-aware Robustness Evaluation of Explainable Deep Learning for Breast Cancer Diagnosis

arXiv:2609.31692 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/NishanCharlie/Explainable-AI

Terminology

• Transformer — 44× in this paper · explained in 2 episodes
• Explainability — 25× in this paper · explained in 3 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Interpretability — 11× in this paper · explained in 15 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Hybrid Architectures — 6× in this paper · explained in 3 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Attention Mechanism — 4× in this paper · explained in 2 episodes
• Integrated Gradients — 4× in this paper
• Representation Learning — 4× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Model-Agnostic — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Hybrid Models — 2× in this paper · explained in 2 episodes
• Mean Coverage — 2× in this paper
• Pointing Game — 2× in this paper
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 2× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes

Sources

• Striving for Simplicity: The All Convolutional Net
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• Towards better understanding of gradient-based attribution methods for Deep Neural Networks

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