Deep Transfer Learning for Interpretable Chest X-Ray Diagnosis

  1. C. Lago 1
  2. I. Lopez-Gazpio 1
  3. E. Onieva 1
  1. 1 Universidad de Deusto
    info

    Universidad de Deusto

    Bilbao, España

    ROR https://ror.org/00ne6sr39

Livre:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Éditorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Année de publication: 2021

Pages: 524-537

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Type: Communication dans un congrès

DIALNET GOOGLE SCHOLAR

Résumé

This work presents an application of different deep learning related paradigms to the diagnosis of multiple chest pathologies. Within the article, the application of a well-known deep Convolutional Neural Network (DenseNet) is used and fine-tuned for different chest X-Ray medical diagnosis tasks. Different image augmentation methods are applied over the training images to improve the performance of the resulting model as well as the incorporation of an explainability layer to highlight zones of the X-Ray picture supporting the diagnosis. The model is finally deployed in a web server, which can be used to upload X-Ray images and get a real-time analysis.The proposal demonstrates the possibilities of deep transfer learning and convolutional neural networks in the field of medicine, enabling fast and reliable diagnosis. The code is made publicly available (https://github.com/carloslago/IntelligentXray - for the model training, https://github.com/carloslago/IntelligentXray Server - for the server demo).

La source de données: Dialnet