System for EIT reconstruction based on Machine Learning techniques

  1. Martín Aller Dominguez
  2. David Mera Pérez
  3. José Manuel Cotos Yáñez
  4. Ledicia Díaz Lago
Libro:
Actas de las XXV Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2021): [Málaga, 22 al 24 de septiembre de 2021]
  1. Rafael Capilla (coord.)
  2. Maider Azanza (coord.)
  3. Miguel Rodríguez Luaces (coord.)
  4. María del Mar Roldán García (coord.)
  5. Loli Burgueño (coord.)
  6. José Raúl Romero (coord.)
  7. José Antonio Parejo Maestre (coord.)
  8. José Francisco Chicano García (coord.)
  9. Marcela Genero (coord.)
  10. Oscar Díaz (coord.)
  11. José González Enríquez (coord.)
  12. Mª Carmen Penadés Gramaje (coord.)
  13. Silvia Abrahão (col.)

Editorial: Sociedad de Ingeniería de Software y Tecnologías de Desarrollo de Software (SISTEDES)

Ano de publicación: 2021

Congreso: Jornadas de Ingeniería del Software y Bases de Datos (JISBD) (25. 2021. Malaga)

Tipo: Achega congreso

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Resumo

Electrical Impedance Tomography (EIT) is a non-invasive technique that can be used to obtain information from inside bodies. To reconstruct internal body images using EIT, it is necessary to solve a mathematical ill-posed problem called inverse problem. We have developed a software called SageTomo that is able to reconstruct EIT images using Machine Learning techniques to solve the inverse problem. Furthermore, SageTomo allows users both to train and store Machine Learning models for EIT reconstruction, as well as to generate and store datasets for training these models.

Fonte de datos: Dialnet