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El. knyga: Medical Information Computing: First MICCAI Meets Africa Workshop, MImA 2024, and First MICCAI Student Board Workshop on Empowering Medical Information Computing and Research through Early-Career Expertise, EMERGE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco

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This book presents a series of revised papers selected from the First MICCAI Meets Africa Workshop, MImA 2024, and First MICCAI Workshop on Empowering Medical Information Computing and Research through Early-Career Expertise, EMERGE 2024, which was held in Marrakesh, Morocco, during October 6, 2024.





MImA 2024 accepted 21 full papers from 45 submissions; for EMERGE 8 papers are included from 9 submissions. They describe cutting-edge research from computational scientists and clinical researchers working on a variety of medical image computing challenges relevant to the African and broader global contexts, as well as emerging techniques for image computing methods tailored to low-resource settings.
First MICCAI Meets Africa Workshop, MImA 2024.- EARLY DETECTION OF LIVER
FIBROSIS.- Optimized Brain Tumor Segmentation for resource constrained
settings: VGG-Infused U-Net Approach.- Optimizing Classification of
Congestive Heart Failure Using Feature Weight Importance Correlation.- MCL:
Multi-Level Consistency Learning for Medical Image
Segmentation.- Trustworthiness for Deep Learning Based Breast Cancer
Detection Using Point-of-Care Ultrasound Imaging in Low-Resource
Settings.- Advancing the Reliability of Ultra-Low Field MRI Brain Volume
Analysis using CycleGAN.- Deep Learning based Non-Invasive Meningitis
Screening using High-Resolution Ultrasound in Neonates and Infants from
Mozambique, Spain and Morocco.- Automated Segmentation of Ischemic Stroke
Lesions in Non-Contrast Computed Tomography Images for Enhanced Early
Treatment and Prognosis.- Spatial Attention-Enhanced Diffusion Model for
Multiple Sclerosis MRI Synthesis.- An Automated Pipeline for the
Identification of Liver Tissue in Ultrasound Video.- Democratizing AI in
Africa: Federated Learning for Low-Resource Edge Devices.- Generative Style
Transfer for MR Image Segmentation: A case of Glioma Segmentation in
Sub-Saharan Africa.- Impact of Skin Tone Diversity on Out-of-Distribution
Detection Methods in Dermatology.- Deployment and Evaluation of Intelligent
DICOM Viewers in Low-Resource Settings: Orthanc Plugin for Semi-Automated
Interpretation of Medical Images.- Enhancing Soil-transmitted Helminths
Diagnosis through AI: A Self-Supervised Learning Approach with
Smartphone-Based Digital Microscopy.- Capturing Complexity of the Foot Arch
Bones: Evaluation of a Statistical Modelling Framework for Learning Shape,
Pose and Intensity Features in a Continuous Domain.- Explainability-Guided
Deep Learning Models For COVID-19 Detection Using Chest X-ray
Images.- Feasibility of Open-Source Tracking-Based Metrics in Evaluating
Ultrasound-Guided Needle Placement Skills in Senegal.- Automatic Segmentation
of Medical Images for Ischemic Stroke in CT Scans for the Identification of
Sulcal Effacement.- AfriBiobank: Empowering Africas Medical Imaging Research
and Practice Through Data Sharing and Governance.- Benchmarking Noise2Void:
Superior Denoising of Medical Microscopic Images.- First MICCAI Workshop on
Empowering Medical Information Computing and Research through Early-Career
Expertise, EMERGE 2024.- Self-consistent deep approximation of retinal traits
for robust and highly effcient vascular phenotyping of retinal colour fundus
images.-Non-Parametric Neighborhood Test-Time Generalization: Application to
Medical Image Classification.- Client Security Alone Fails in
Federated Learning: 2D and 3D Attack Insights.-Context-Guided Medical Visual
Question Answering.- GRAM: Graph Regularizable Assessment
Metric.- Unsupervised Analysis of Alzheimers Disease Signatures using 3D
Deformable Autoencoders.- Deep Feature Fusion Framework for
Alzheimers Disease Staging using Neuroimaging Modalities.- Explainable
Few-Shot Learning for Multiple Sclerosis Detection in Low-Data Regime.