AIMC Topic: Tomography, X-Ray Computed

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Personalized federated learning for abdominal multi-organ segmentation based on frequency domain aggregation.

Journal of applied clinical medical physics Dec 5, 2024
PURPOSE: The training of deep learning (DL) models in medical images requires large amounts of sensitive patient data. However, acquiring adequately labeled datasets is challenging because of the heavy workload of manual annotations and the stringent...
Algorithms Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Abdomen Magnetic Resonance Imaging Federated Learning Humans
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Low-Quality Sensor Data-Based Semi-Supervised Learning for Medical Image Segmentation.

Sensors (Basel, Switzerland) Dec 5, 2024
Traditional medical image sensors face multiple challenges. First, these sensors typically rely on large amounts of labeled data, which are time-consuming and costly to obtain. Second, when the data volume and image size are large, traditional sensor...
Magnetic Resonance Imaging Diagnostic Imaging Supervised Machine Learning Humans Algorithms Tomography, X-Ray Computed Image Processing, Computer-Assisted
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Deep Quasi-Recurrent Self-Attention With Dual Encoder-Decoder in Biomedical CT Image Segmentation.

IEEE journal of biomedical and health informatics Dec 5, 2024
Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient labeled data to train the models. There are many models in t...
Image Processing, Computer-Assisted Algorithms Tomography, X-Ray Computed Deep Learning Humans
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Deep-DM: Deep-Driven Deformable Model for 3D Image Segmentation Using Limited Data.

IEEE journal of biomedical and health informatics Dec 5, 2024
Objective - Medical image segmentation is essential for several clinical tasks, including diagnosis, surgical and treatment planning, and image-guided interventions. Deep Learning (DL) methods have become the state-of-the-art for several image segmen...
Humans Neural Networks, Computer Imaging, Three-Dimensional Magnetic Resonance Imaging Heart Ventricles Algorithms Tomography, X-Ray Computed Deep Learning
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Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique.

Cancer investigation Dec 4, 2024
The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identifica...
Magnetic Resonance Imaging Intracranial Hemorrhages Tomography, X-Ray Computed Risk Factors Neural Networks, Computer Brain Neoplasms Algorithms Male Prognosis Risk Assessment Humans Deep Learning
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Multi-Sensor Learning Enables Information Transfer Across Different Sensory Data and Augments Multi-Modality Imaging.

IEEE transactions on pattern analysis and machine intelligence Dec 4, 2024
Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images...
Brain Tomography, X-Ray Computed Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging Humans Machine Learning Algorithms
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Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy.

Cell reports. Medicine Dec 4, 2024
Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framew...
Treatment Outcome Neoadjuvant Therapy Male Humans Middle Aged Aged Retrospective Studies Artificial Intelligence ROC Curve Prognosis Adult Tomography, X-Ray Computed Female Stomach Neoplasms
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Leveraging deep transfer learning and explainable AI for accurate COVID-19 diagnosis: Insights from a multi-national chest CT scan study.

Computers in biology and medicine Dec 3, 2024
The COVID-19 pandemic has emerged as a global health crisis, impacting millions worldwide. Although chest computed tomography (CT) scan images are pivotal in diagnosing COVID-19, their manual interpretation by radiologists is time-consuming and poten...
Reproducibility of Results Pneumonia, Viral SARS-CoV-2 Deep Learning Pandemics Radiographic Image Interpretation, Computer-Assisted COVID-19 Coronavirus Infections Humans Tomography, X-Ray Computed Betacoronavirus Neural Networks, Computer
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Deep learning versus human assessors: forensic sex estimation from three-dimensional computed tomography scans.

Scientific reports Dec 3, 2024
Cranial sex estimation often relies on visual assessments made by a forensic anthropologist following published standards. However, these methods are prone to human bias and may be less accurate when applied to populations other than those for which ...
Tomography, X-Ray Computed Female Young Adult Humans Deep Learning Imaging, Three-Dimensional Indonesia Adult Male Middle Aged Sex Determination by Skeleton Skull Forensic Anthropology
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DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: Pancreas.

PloS one Dec 3, 2024
The utilization of artificial intelligence (AI) is expanding significantly within medical research and, to some extent, in clinical practice. Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable pote...
Image Processing, Computer-Assisted Pancreas Algorithms Humans Tomography, X-Ray Computed Neural Networks, Computer Deep Learning
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