Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
cs.CV
Submitted: 2024-10-03
Updated: 2026-09-26
Terminology
Sources
- AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans
- Concept-based Explainable Artificial Intelligence: A Survey
- Extending Logic Explained Networks to Text Classification
- Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis
- ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
- XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
- GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
- HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
- WoLF: Wide-scope Large Language Model Framework for CXR Understanding
- A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
- Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images
- Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
- Towards A Rigorous Science of Interpretable Machine Learning
- Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning
- On quantitative aspects of model interpretability
- Towards Expert-Level Medical Question Answering with Large Language Models
- MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering
- Automated Natural Language Explanation of Deep Visual Neurons with Large Models
- PathVQA: 30000+ Questions for Medical Visual Question Answering
- Hierarchical 3D fully convolutional networks for multi-organ segmentation
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