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Related Experiment Video

Updated: Aug 10, 2025

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

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Ensemble-based genetic algorithm explainer with automized image segmentation: A case study on melanoma detection

Hossein Nematzadeh¹, José García-Nieto², Ismael Navas-Delgado²

¹ITIS Software, Universidad de Málaga, Arquitecto Francisco Peñalosa 18, Malaga, 29071, Spain; Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain.

Computers in Biology and Medicine

|February 10, 2023

Summary

This summary is machine-generated.

This study introduces the Ensemble-based Genetic Algorithm Explainer (EGAE) to automate image segmentation for explainable AI (XAI) in melanoma detection. EGAE improves explanation accuracy and efficiency compared to LIME.

Keywords:

Deep learning Explainable Artificial Intelligence Genetic algorithm Local Interpretable Model-agnostic Explanations Melanoma dataset

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Artificial Intelligence
Medical Imaging
Computational Biology

Background:

Explainable Artificial Intelligence (XAI) is crucial for understanding complex AI models, especially in medical applications.
Local Interpretable Model-agnostic Explanations (LIME) offers an image explainer but requires manual parameter tuning, which is time-consuming.
Automating image segmentation in XAI can enhance efficiency and accuracy for tasks like melanoma detection.

Purpose of the Study:

To propose an automated image segmentation method for explainable AI in melanoma detection.
To develop the Ensemble-based Genetic Algorithm Explainer (EGAE) to identify and present informative image sections automatically.
To compare the performance of EGAE with existing methods like LIME in terms of accuracy and efficiency.

Main Methods:

The proposed Ensemble-based Genetic Algorithm Explainer (EGAE) automates image segmentation for deep learning models.
EGAE employs a three-phase approach: heuristic determination of GA sparsity, consecutive execution of multiple GAs with varying superpixels, and ensembling results.
Euclidean distance is utilized to measure the accuracy between expert-delineated explanations and explainer-computed explanations.

Main Results:

EGAE successfully automates the detection of informative lesions in melanoma images.
The proposed method demonstrates improved explanation accuracy compared to LIME.
Experimental results on a melanoma dataset validate the efficiency and effectiveness of EGAE.

Conclusions:

EGAE offers an efficient and accurate solution for automated image segmentation in explainable AI for melanoma detection.
The developed method reduces the manual effort required for parameter tuning in image explainers.
EGAE contributes to more understandable and reliable AI-driven diagnostic tools in healthcare.