This paper includes the code to train the models for detecting anomalies in cytology images.

• The anomalib folder contains the code to train the models using the anomalib library.
• The reconstruction folder contains the code to train the models using a GAN model using the FastAI library.
• The ImageJ-macro folder contains the code to run the anomaly detection model from Python.
• The inference folder contains the code to use the model for predictions using a folder.
• There is a repository with the Python application

If you use this new version and code, please, cite as follows:

  @article{2024,   
  title       = {Anomaly Detection applied to the Classification of Cytology Images},  
  author       = {Carlo Bruno Marta and Manuel Doblaré and Jónathan Heras and Gadea Mata and Teresa Ramírez},   
  journal = {Under review},
  year      = {2024}
  }

Jónathan Heras, Universidad de La Rioja

jonathan.heras AT unirioja.es

This work was partially supported by Grant PID2020-115225RB-I00 funded by MCIN/AEI/10.13039/501100011033, Agencia de Desarrollo Económico de La Rioja ADER 2022-I-IDI-00015 and by the Government of La Rioja through Proyecto Inicia 2023/01.