Call for Papers

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.