Titlebook: Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Bio; First MICCAI Worksho Siva Teja Kakile

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0302-9743 uss this specific sub-topic of AI over Infrared Images for Medical Applications at MICCAI and promote this novel area of research that has the potential to hugely impact our society, among the research community..978-3-031-19659-1978-3-031-19660-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Pruritus

Ziquan Yu,Youmin Zhang,Bin Jiang,Chun-Yi Su51 volunteers with inflammatory toothache and 25 volunteers compatible with normality. They were submitted to thermographic, panoramic radiographic and clinical examinations. Among the four models tested, MobileNetV2 performed better in both front and lateral views. However, considering the ROC curv

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https://doi.org/10.1007/978-1-4614-2113-9ck of open source datasets is an issue to develop such systems. In this paper, we discuss our work on using machine learning over thermal video streams for face and mask detection and subsequent temperature screening in a passive non-invasive way that enables an effective automated COVID-19 screenin

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https://doi.org/10.1007/978-3-030-12846-3le sclerosis (MS) clinical trial magnetic resonance imaging (MRI) dataset of relapsing-remitting (RRMS) patients, we demonstrate that our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the popula

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Fault-Tolerant Module-Based Compilationvolume circumscribing the major liver vessels, from which a new imaging biomarker, B. is derived. Additional biomarkers are extracted and leveraged to train and evaluate a LR complexity prediction model. An ablation study shows the HCZ-based biomarker as the central feature in predicting LR complexi

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Fault-Tolerant Digital Microfluidic Biochipsn two large independent rectal cancer datasets of patients selectively treated with radiotherapy and capecitabine in two UK clinical trials. Experiments demonstrate that both models, PREViT and ClusterViT, show improvements in the prediction over baseline models.