arXiv (OAI Expanded)
open access
Generating Medical Image Counterfactuals using Causal Explanations
Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by limited explainability. Counterfactual images provide one means of auditing model behavior by showing how an image would need to change for a classif
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Generating Medical Image Counterfactuals using Causal Explanations#924837
- Architecture-aware Robustness Evaluation of Explainable Deep Learning for Breast Cancer Diagnosis#1115648
- FCA-Guided Counterfactual Explanations for Multi-Modal Breast Cancer Diagnosis: A Framework Achieving Perfect Validity with Emergent Sparsity#1033845
- Faithful Faithfulness Evaluations: Challenges & Pitfalls Learned from a Breast MRI Case Study#1061883
- Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU#668039
Record · ID 920603
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive