PumaMedNet-CXR: An Explainable Generative Artificial Intelligence for the Analysis and Classification of Chest X-Ray Images

Authors

  • Carlos Minutti-Martinez Universidad Nacional Autónoma de México
  • Boris Escalante-Ramírez Universidad Nacional Autónoma de México
  • Jimena Olveres-Montiel Universidad Nacional Autónoma de México

DOI:

https://doi.org/10.13053/cys-27-4-4777

Keywords:

Medical image analysis, autoencoder, explainable artificial intelligence, chest X-Ray

Abstract

In this paper, we introduce PumaMedNet -CXR, a generative AI designed for medical image classification, with a specific emphasis on Chest X-ray (CXR) images. The model effectively corrects common defects in CXR images, offers improved explainability, enabling a deeper understanding of its decision-making process. By analyzing its latent space, we can identify and mitigate biases, ensuring a more reliable and transparent model. Notably, PumaMedNet-CXR achieves comparable performance to larger pre-trained models through transfer learning, making it a promising tool for medical image analysis. The model’s highly efficient autoencoder-based architecture, along with its explainability and bias mitigation capabilities, contribute to its significant potential in advancing medical image understanding and analysis.

Author Biographies

Carlos Minutti-Martinez, Universidad Nacional Autónoma de México

CECAv

Boris Escalante-Ramírez, Universidad Nacional Autónoma de México

Laboratorio Avanzado de Procesamiento de Imagenes

Jimena Olveres-Montiel, Universidad Nacional Autónoma de México

Laboratorio Avanzado de Procesamiento de Imagenes

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Published

2023-12-17

Issue

Vol. 27 No. 4 (2023): 27(4) 2023

Section

Articles

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