CfP: 2nd MICCAI Workshop on “Affordable healthcare and AI for Resource diverse global health” (FAIR)
Albarqouni, Shadi
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
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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 | shadi.al...@ukbonn.de | albarqouni.github.io
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