Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

• URL: http://arxiv.org/abs/2410.02331v1
• Date: Thu, 3 Oct 2024 09:29:28 GMT
• Title: Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
• Authors: Junlin Hou, Sicen Liu, Yequan Bie, Hongmei Wang, Andong Tan, Luyang Luo, Hao Chen,
• Abstract summary: Self-eXplainable AI (S-XAI) incorporates explainability directly into the training process of deep learning models. This paper outlines the desired characteristics of explainability and existing evaluation methods for assessing explanation quality.
• Score: 9.93411316886105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to explain black-box models after training, have been controversial in recent works concerning their fidelity to the models' predictions. In contrast, Self-eXplainable AI (S-XAI) offers a compelling alternative by incorporating explainability directly into the training process of deep learning models. This approach allows models to generate inherent explanations that are closely aligned with their internal decision-making processes. Such enhanced transparency significantly supports the trustworthiness, robustness, and accountability of AI systems in real-world medical applications. To facilitate the development of S-XAI methods for medical image analysis, this survey presents an comprehensive review across various image modalities and clinical applications. It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing counterfactual explanation and textual explanation. Additionally, this paper outlines the desired characteristics of explainability and existing evaluation methods for assessing explanation quality. Finally, it discusses the major challenges and future research directions in developing S-XAI for medical image analysis.

Related papers

• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care [2.7195102129095003]
  Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. Deep-learning models are complex and are often treated as a "black box" that can cause uncertainty regarding how they operate. This review summarises recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT.
  arXiv  Detail & Related papers  (2023-09-01T05:01:52Z)
• Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review [4.918419052486409]
  Existing artificial intelligence models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability. Recently, explainable artificial intelligence (XAI) has emerged as a specialized technique that can provide confidence in the model's prediction. This paper presents the first survey of XAI techniques used for knee OA diagnosis.
  arXiv  Detail & Related papers  (2023-08-18T08:23:47Z)
• XAI Renaissance: Redefining Interpretability in Medical Diagnostic Models [0.0]
  The XAI Renaissance aims to redefine the interpretability of medical diagnostic models. XAI techniques empower healthcare professionals to understand, trust, and effectively utilize these models for accurate and reliable medical diagnoses.
  arXiv  Detail & Related papers  (2023-06-02T16:42:20Z)
• A Brief Review of Explainable Artificial Intelligence in Healthcare [7.844015105790313]
  XAI refers to the techniques and methods for building AI applications. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices.
  arXiv  Detail & Related papers  (2023-04-04T05:41:57Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Explainable Deep Learning Methods in Medical Image Classification: A Survey [0.0]
  State-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data. These models are hardly adopted in clinical, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models.
  arXiv  Detail & Related papers  (2022-05-10T09:28:14Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models [85.4333256782337]
  VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow. We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence. We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
  arXiv  Detail & Related papers  (2021-08-04T17:34:13Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.