CfP: 2nd MICCAI Workshop on “Affordable healthcare and AI for Resource diverse global health” (FAIR)

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Albarqouni, Shadi

May 4, 2022, 4:55:59 AM5/4/22
Dear all, 

I’m happy to share with you the CfP for our 2nd MICCAI Workshop on Affordable healthcare and AI for Resource Diverse Global Health. Please spread the word and share the call with your faculty members and who may have interests. 

Best wishes,



2nd MICCAI Workshop on “Affordable healthcare and AI for Resource diverse global health” (FAIR)

Singapore, 18 Sept. 2022


Call for Papers: regular papers and short white papers


More information in:
Twitter: @FairWorkshop

In this workshop, we aim to attract technical contributions (regular papers -12 pages), addressing medical problems of emerging and developing countries via algorithms, with a sharp focus on affordability, for example, high model performance/accuracy using low-computational resources and limited technology/infrastructure. Areas of application in medicine include but are not limited to:

  • Limited data generated by low infrastructure, e.g., poor quality, low resolution, missing slices, incomplete scans, communication bandwidth issues challenging bulky data transmission, regulatory hurdles to sharing data on cloud …etc.
  • Basic imaging modalities/facilities (X-rays, ultrasound, retinal scans, microscopy, Optical imaging, e.g. Skin Lesion, Fundus, …etc.)
  • Low-cost portable cameras and smart-phone based camera imaging and videos for diagnosis
  • Biosignals (Stethoscope, EEG, ECG,…etc.)
  • Minimal medical and computational resources for diagnosis using basic imaging facilities

With methodological contributions spanning different sub-fields such as:

  • Affordable Image to Image Translation (e.g., the low image quality of low-cost device to high image quality solution)
  • Affordable annotation-efficient DL Models (e.g., unsupervised, semi-supervised,…etc.) Handling data heterogeneity (e.g., missing and noisy data)
  • Affordable Domain Adaptation and Transfer Learning Affordable Continual and Meta-Learning
  • Affordable Bias-resilient and Fairness (e.g., measures to identify biases)
  • Affordable Model Compactness and Compression for limited energy and lower-end devices.
  • Affordable Interpretable and Trustable AI Models
  • Affordable Multimodal data (Imaging, Biosignal, EHR/EMR, Genomics, multi-Omics, wearable sensors)

Besides, we will also accept white papers (4-6 page limit), focusing on:

  • Introducing and identifying the AI challenges/opportunities in Healthcare with low resources
  • Presenting past, ongoing, or potential real-world experience on FAIR
  • Introduce new strategies for democratizing AI and making it affordable in low R&D countries and everywhere
  • Driving of Artificial Intelligence “AI” in the healthcare of the future societies, and the emerging debates on the democratization of ethical and FAIR AI.
  • Limited open data from low R&D countries: collection and sharing policies, security, acquisition protocols, etc
  • Making AI affordable for Healthcare and making Healthcare affordable with AI
We will compile all white papers into digital proceedings (PDF) which will be published on our FAIR-MICCAI website from year to year including all past editions. This will allow the MICCAI community to be spot-on on challenging topics in affordable AI with limited resources across all continents as well as trace back recurring issues to solve.

Important Dates:
- Submission Deadline: 25 June 2022
- Notification of Acceptance: 16 July 2022
- Camera Ready Deadline: 30 July 2022
- Workshop: 18 September 2022




Shadi Albarqouni, University Hospital Bonn & Helmholtz AI, Germany
Sophia Bano, University College London, United Kingdom
Yenisel Plasencia Calana, Maastricht University, Netherlands
Bishesh Khanal, NepAl Applied Mathematics and Informatics Institute for Research, Nepal
Linda Marrakchi-Kacem, National Engineering School of Tunis, Tunisia
Yunusa Mohammed, Gombe State University Nigeria & Algorizmi health ltd., Nigeria 
Islem Rekik, Istanbul Technical University, Turkey
Nicola Rieke, NVIDIA GmbH, Germany
Aya Salama, Algorithm tech & American University in Cairo, Egypt
Farah Shamout, NYU Abu Dhabi, United Arab Emirates
Debdoot Sheet, Indian Institute of Technology Kharagpur, India 


Shadi Albarqouni, Ph.D., SMIEEE 

Professor for Computational Medical Imaging Research (CIR Professor) @UniBonn

AI Young Investigator Group Leader @HelmholtzAI

+49 (0) 228 287 19089 | |

Lab. @UniKlinikum Bonn

Building 07, Department of Diagnostic and Interventional Radiology, University Hospital Bonn · Venusberg-Campus 1 · 53127 Bonn  

Lab. @HelmholtzAI

Building 85a, Helmholtz Center Munich · Ingolstädter Landstr. 1 · 85764 Neuherberg

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