Explainable Deep Learning for COVID-19 data

Organized by Raúl Cruz Barbosa, Technological University of the Mixteca Region, México and Alfredo Vellido, Polytechnic University of Catalonia, Spain

Salón 213. Claustro de la Merced, Universidad de Cartagena  (Google Maps).
Cartagena de Indias, Colombia. November 23, 2022

Explainable Artificial Intelligence and Machine Learning (XAI) methods are very important in many human-computer interaction contexts. In the medical applications domain in particular, they become a must. On the other hand, to face some problems related to the coronavirus disease 2019 (COVID-19) pandemic through a data-centred approach, the deep learning and medical image analysis communities have developed and disseminated models and tools for preprocessing, segmentation and classification tasks from COVID-19 imaging data. However, recent review papers have shown that most of them have no potential clinical use due to several flaws (as methodology or underlying biases). To address these problems at least partially, explainability is a key factor that can boost the medical acceptability of deep learning models.

Therefore, the goal of this workshop is to gather researchers working on the development of deep learning models mainly for COVID-19 detection, diagnosis or prognosis that include an explanation method (using an interpretable-by-design model and/or post hoc explanation, for instance). We also welcome research that goes beyond COVID to address other epidemiological problems and research that focuses on the medical decision making processes involved in XAI.


Call for  Papers

We welcome methodological contributions, case studies, position papers and early research reports.WorkshopExplainableDeepLearning

The list of topics addressed in the workshop include, but are not limited to:

  • Interpretable-by-design deep learning models.
  • Locally Interpretable Model Explanations (LIME) and deep learning
  • Occlusion Sensitivity and deep learning
  • Activation maps (Grad-CAM and its variations) and deep learning
  • Gradient-based methods (guided backpropagation, saliency maps) and deep learning
  • Feature selection and explanation methods.
  • New curated COVID-19 benchmarks and datasets.
  • Deep learning in epidemiology.
Important dates and submission procedure:
  • Submission Deadline: October 15, 2022
  • Notification Date: November 1, 2022
  • Camera-Ready Deadline: November 13, 2022
  • Workshop date: November 23, 2022 [tentative schedule from 9:00 to 17:00]

Papers will be submitted to the organizers via email (rcruz@mixteco.utm.mx and avellido@cs.upc.edu ), following the submission guidelines of the IBERAMIA  conference (see https://www.iberamia.org/iberamia/iberamia2022/call-for-papers/). The papers may be written in Spanish, Português, or English. Papers will be peer-reviewed. The workshop is expected to foster discussion and therefore the paper may include preliminary contributions. All accepted papers will be uploaded to the workshop site.

IMPORTANT: At least one author is required to register for the workshop and be available to present the paper. The selected works will have 15-20 minutes for the presentation and 5 minutes for questions.


Deadline: November, 6,  2022

Workshop Fee: No fee, for those also registered in Iberamia’2022.  For those who just want to register for the workshop: €50 (It Includes the material provided by the organizers, access to the workshop and coffee-breaks)

In order to process your registration, please folllow these steps:

  1. Fill and Send the Registration Form and then
  2. Proceed with the Payment (only, if you are not also registered in Iberamia’2022). You can pay by Bank Transfer or Pay on-site.

(Step 1) Registration Form

(if there is any problem in sending this form, please send the same data by mail to iberamia@iberamia.org, with the subject «Explainable Deep Learning for COVID-19 data”).

    (Please, fill all required data)

    Last Name (Required. Example Smith, Perez):

    First Name (Required. Example John, Pedro):

    Affiliation (Required. Example: University of... ):


    City / State:

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    ADDITIONAL INFO (Optional):

    (Step 2) Payment Methods

    if you want to apply for a scholarship, (send an e-mail to  organizers: rcruz@mixteco.utm.mx and avellido@cs.upc.edu )

    Bank Transfer:


    IBAN: ES20 1490 0001 1124 1014 1934    



    Address: C/ Goya, 11. 28001 Madrid (Spain)

    Important: In case of Bank Transfer, you should send us a copy of the bank deposit to: iberamia@iberamia.org in order to complete the registration (Subject: Workshop Explainable Deep Learning for COVID-19 data).


    You can pay at the Conference Venue.