
Artificial Intelligence over Infrared Images for Medical Applications
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This book constitutes the refereed proceedings of the 4th International Conference on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2025, held as a virtual event, on November 15, 2025.
The 13 full papers presented in these proceedings were carefully reviewed and selected from 23 submissions. They focus on the application of artificial intelligence in medical infrared imaging for cancer screening, cancer diagnosis, cancer risk assessment, treatment monitoring, sports injury, and pain management.
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Content
.- Video Transformers for Dynamic Breast Infrared Imaging Classification.
.- Multi-View Thermal Breast Imaging for Malignancy Detection: Performance Benchmarking of CNN, Transformer, and Involution Architectures.
.- A Shapley Value-based Gated Feature Fusion of Multi-branch Deep Learning Framework for Breast Cancer Screening of Thermal Images.
.- An End-to-End GAN-CNN Framework for Early Breast Anomaly Detection Using Thermal Imaging.
.- Interpretable Breast Cancer Risk Stratification using Statistical Feature Engineering on Thermal Images.
.- Functional Imaging-Guided Early Detection of Radiation Pneumonitis: A Multimodal Framework Integrating CT Radiomics and Infrared Thermograph.
.- Customized CNN Based Multiclass Classification of Childhood Obesity Using Infrared Thermal Imaging.
.- A Non- Invasive Diagnostic Approach to Orofacial Pain using Infrared Thermography and Machine Learning.
.- Thermal Asymmetry in Football Players Following Ankle Injury: Findings Related to Training Load.
.- Beyond Symmetry: Defining Normative Inter-Regional Patterns in Infrared Thermography.
.- AI-Assisted Multispectral Infrared Imaging and Robotic Telerounding for Postoperative Assessment Using Aillumi Generative Intelligence.
.- Presenting a Dataset for 3D Breast Surface Representation Using Thermography.
.- MedThermalDICOM: An Open-Source DICOM-Compliant Framework for Medical Thermal Imaging Enabling Clinical Integration and Research Reproducibility.
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