Titlebook: Applications of Medical Artificial Intelligence; First International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2022 T

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M.-H. Goni,V. Markussis,G. Tolisikely fractured regions. Based on this fracture probability map we detect the presence of fracture and are able to differentiate a fractured tooth from a control tooth. We compare these results to a 2D CNN-based approach and we show that our approach provides superior detection results. We also show

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,Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA,ed invasive examinations to assess this condition, the current research focus is put on non-invasive procedures. Here, the coronary computed tomography angiography is the first-choice modality, but its manual analysis is cost-inefficient, lacks reproducibility, and suffers from significant inter- an

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Automated Assessment of Renal Calculi in Serial Computed Tomography Scans,his retrospective study included 722 scans from 330 patients chosen from 8544 asymptomatic patients who underwent two or more CTC (CT colonography) or non-enhanced abdominal CT scans between 2004 and 2016 at a single medical center. A pre-trained deep learning (DL) model was used to segment the kidn

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,Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learningr (HNC) patients treated with radiotherapy (RT). The prediction of mandibular ORN may not only guide the RT treatment planning optimisation process but also identify which patients would benefit from a closer follow-up post-RT for an early diagnosis and intervention of ORN. Existing mandibular ORN p

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,Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development,ge. This paper provides an overview of the potential biases that appear in image analysis datasets that affect the development and performance of artificial intelligence algorithms. Especially, an exhaustive analysis of mammography data has been carried out at the patient, image and source of origin

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,ECG-ATK-GAN: Robustness Against Adversarial Attacks on ECGs Using Conditional Generative Adversariaearning approaches have reached human-level performance in classifying arrhythmia from ECGs. However, these architectures are vulnerable to adversarial attacks, which can misclassify ECG signals by decreasing the model’s accuracy. Adversarial attacks are small crafted perturbations injected in the o