Explainable AI (XAI) Techniques for Semantic Image Segmentation: A Comprehensive Survey Across Medical, Industrial, and Other Applications

Concetti Chiave

This survey provides a comprehensive overview of the latest techniques for making semantic image segmentation models more interpretable and explainable, with applications spanning medical, industrial, and other domains.

Sintesi

This survey presents a comprehensive review of the state-of-the-art in explainable AI (XAI) techniques for semantic image segmentation. It covers a wide range of applications, from medical imaging to industrial monitoring, and proposes a taxonomy to categorize the different XAI methods.

The key highlights and insights from the survey are:

1. Taxonomy of XAI methods for image segmentation:

   • Prototype-based: Use representative samples or parts from the dataset to analyze and compare with the input image.
   • Gradient-based: Calculate the gradient of the output with respect to the input or intermediate feature maps.
   • Perturbation-based: Measure the effect of occluding or deactivating parts of the input or feature maps on the model's output.
   • Counterfactual: Generate the minimum input changes needed to change the model's output.
   • Architecture-based: Introduce architectural changes to make the model more inherently interpretable.

2. Applications in medical imaging:

   • Dermatology: Interpret melanoma diagnosis and localize biomarker indicators.
   • Forensic medicine: Segment post-mortem iris images.
   • Gastroenterology: Explain polyp and medical instrument segmentation.
   • Hepatology: Cross-explain liver segmentation between different DL models.
   • Oncology: Interpret brain, liver, and lung tumor segmentation.
   • Ophthalmology: Segment retinal layers, glaucoma, and diabetic macular edema.

3. Industrial and other applications:

   • Remote sensing: Segment land cover, crops, and environmental features.
   • Biometrics: Explain iris and footprint segmentation.
   • Self-supervised segmentation: Use XAI to guide the learning of more interpretable models.

4. Evaluation of XAI methods:

   • Qualitative evaluation through user studies and visual comparisons.
   • Quantitative evaluation using metrics like infidelity, sensitivity, deletion, and insertion curves.
   • Challenges in reliable quantitative evaluation due to high variance and sensitivity to implementation details.

The survey highlights the growing importance of interpretable and explainable AI in real-world applications, especially in high-stakes domains like healthcare and safety-critical industries. It provides a comprehensive overview of the latest advancements in this field and identifies future research directions.

Statistiche

"The proportion of companies adopting AI has more than doubled from 2017 to 2022."
"In 2022, the medical and healthcare sectors have attracted the most investment, with a total of 6.1 billion dollars."
"83% of surveyed IT professionals stated that it is important to their business to explain how their AI reached the decision."

Citazioni

"XAI is not a new development, particularly in rule-based expert systems and machine learning, but it has experienced unprecedented growth ever since the revived interest in neural networks."
"Semantic image segmentation is an essential task in computer vision, with applications ranging from autonomous driving to medical image analysis."
"Interpretability in semantic image segmentation is a challenging area of study. On one hand, it can be viewed as an extension of a relatively intuitive interpretable classification. However, it requires combining the relative influence of each classified pixel of interest."

Approfondimenti chiave tratti da

Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey

by Roka... alle arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01636.pdf

Domande più approfondite

How can XAI techniques be extended to handle the inherent uncertainty and inter-observer variability in semantic image segmentation, especially in medical applications?

In semantic image segmentation, especially in medical applications where precision is crucial, handling inherent uncertainty and inter-observer variability is essential. XAI techniques can be extended in the following ways:

Probabilistic Models: Introducing probabilistic models can help capture uncertainty in segmentation predictions. By providing confidence intervals or probability distributions over segmentation outputs, these models can convey the uncertainty associated with each prediction.

Ensemble Methods: Utilizing ensemble methods can help mitigate uncertainty by combining multiple segmentation models to provide more robust and reliable predictions. By aggregating the outputs of diverse models, ensemble methods can capture different sources of variability and uncertainty.

Bayesian Deep Learning: Bayesian deep learning techniques can incorporate uncertainty estimation directly into deep neural networks. By modeling uncertainty in network weights and predictions, Bayesian approaches can provide more reliable and interpretable segmentation results.

Uncertainty Quantification: Implementing methods for uncertainty quantification, such as Monte Carlo dropout or variational inference, can help estimate the uncertainty in segmentation predictions. These techniques can provide insights into the model's confidence in its predictions.

Interpretability Techniques: Leveraging interpretability techniques like prototype-based methods or counterfactual explanations can help in understanding and addressing inter-observer variability. By providing intuitive explanations for segmentation decisions, these methods can enhance the transparency and trustworthiness of the segmentation model.

By incorporating these extensions into XAI techniques for semantic image segmentation, especially in medical applications, it is possible to better handle inherent uncertainty and inter-observer variability, leading to more reliable and trustworthy segmentation results.

What are the potential limitations and biases introduced by the current XAI methods, and how can they be addressed to ensure the reliability and trustworthiness of the explanations?

Current XAI methods, while valuable for providing insights into model decisions, come with limitations and biases that can impact the reliability and trustworthiness of the explanations. Some potential limitations and biases include:

Saliency Map Interpretation: Saliency maps generated by gradient-based methods may not always accurately reflect the model's decision-making process, leading to misinterpretations of important features.

Confirmation Bias: XAI methods may exhibit confirmation bias, where explanations that align with preconceived notions are favored over more accurate but unexpected explanations.

Adversarial Attacks: XAI methods can be vulnerable to adversarial attacks, where small perturbations to input data lead to significant changes in the explanation, potentially undermining the trustworthiness of the model.

Limited Scope: Some XAI methods may have a limited scope and may not capture the full complexity of the model, leading to incomplete or biased explanations.

To address these limitations and biases and ensure the reliability and trustworthiness of XAI explanations, the following strategies can be implemented:

Robustness Testing: Conduct robustness testing to evaluate the resilience of XAI methods against adversarial attacks and ensure that explanations remain consistent under different conditions.

Diverse Evaluation: Use diverse evaluation metrics and datasets to validate the performance of XAI methods across various scenarios and ensure their generalizability.

Human-in-the-Loop: Incorporate human-in-the-loop approaches to validate XAI explanations with domain experts and end-users, ensuring that the interpretations align with domain knowledge.

Transparency and Documentation: Provide transparency in the XAI process by documenting the methodology, assumptions, and limitations of the explanation techniques used, enhancing the trustworthiness of the explanations.

By addressing these limitations and biases through rigorous testing, diverse evaluation, human validation, and transparency, the reliability and trustworthiness of XAI explanations can be enhanced.

Given the growing emphasis on AI regulation and the need for trustworthy AI systems, how can the insights from this survey on explainable segmentation be leveraged to inform the development of standardized evaluation frameworks and guidelines for XAI in high-stakes applications?

The insights from the survey on explainable segmentation can play a crucial role in informing the development of standardized evaluation frameworks and guidelines for XAI in high-stakes applications in the following ways:

Establishing Best Practices: The survey can identify best practices and effective XAI techniques for segmentation, which can serve as a foundation for developing standardized evaluation frameworks. By highlighting successful approaches and methodologies, guidelines can be established for evaluating the reliability and interpretability of XAI systems.

Defining Evaluation Metrics: The survey can help define key evaluation metrics and criteria for assessing the performance of XAI methods in segmentation tasks. By identifying relevant metrics such as accuracy, robustness, and interpretability, standardized evaluation frameworks can be designed to ensure the quality and trustworthiness of AI systems.

Guiding Regulatory Compliance: Insights from the survey can guide the development of regulatory compliance frameworks for AI systems in high-stakes applications. By incorporating recommendations from the survey on explainable segmentation, regulatory bodies can establish guidelines for ensuring transparency, accountability, and ethical use of AI technologies.

Promoting Interdisciplinary Collaboration: The survey can facilitate interdisciplinary collaboration between AI researchers, domain experts, policymakers, and ethicists to develop comprehensive guidelines for XAI in high-stakes applications. By fostering collaboration across diverse fields, standardized evaluation frameworks can be designed to address the complex challenges of trustworthy AI systems.

By leveraging the insights from the survey on explainable segmentation to inform the development of standardized evaluation frameworks and guidelines, stakeholders can ensure the responsible and ethical deployment of AI technologies in high-stakes applications, promoting transparency, accountability, and trust in AI systems.