AIMC Topic: Tomography, X-Ray Computed

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Radiomics-based machine learning for automated detection of Pneumothorax in CT scans.

PloS one Dec 9, 2024
The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by ...
ROC Curve Pneumothorax Tomography, X-Ray Computed Male Female Humans Young Adult Aged Middle Aged Machine Learning Adult Algorithms Adolescent Radiomics
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[Application of artificial intelligence-based organ contouring software and comparison of results in treatment planning for radiotherapy].

Orvosi hetilap Dec 8, 2024
Humans Radiotherapy Planning, Computer-Assisted Software Tomography, X-Ray Computed Artificial Intelligence Radiotherapy Dosage Neoplasms Algorithms Organs at Risk
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Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.

Abdominal radiology (New York) Dec 7, 2024
PURPOSE: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).
Contrast Media Body Composition Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Adult Male Middle Aged Female Aged Aged, 80 and over Humans Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Algorithms Biomarkers
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Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging.

BMC cancer Dec 6, 2024
RATIONALE AND OBJECTIVES: The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imagin...
Retrospective Studies Tomography, X-Ray Computed Female Aged Kidney Diseases, Cystic Adult Radiomics Humans Male Middle Aged Support Vector Machine Diagnosis, Differential Kidney Neoplasms Carcinoma, Renal Cell
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Self-supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences.

Medical physics Dec 5, 2024
BACKGROUND: Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled examples. Self-pretraining is a SSL approach that uses curated do...
Lung Neoplasms Carcinoma, Non-Small-Cell Lung Supervised Machine Learning Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer
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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...
Deep Learning Image Processing, Computer-Assisted Abdomen Magnetic Resonance Imaging Humans Algorithms Tomography, X-Ray Computed Federated Learning
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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...
Algorithms Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Diagnostic Imaging Supervised Machine Learning
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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...
Deep Learning Image Processing, Computer-Assisted Humans Algorithms Tomography, X-Ray Computed
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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 Deep Learning Imaging, Three-Dimensional Magnetic Resonance Imaging Heart Ventricles Neural Networks, Computer Algorithms Tomography, X-Ray Computed
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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...
Risk Factors Algorithms Brain Neoplasms Magnetic Resonance Imaging Prognosis Male Humans Risk Assessment Intracranial Hemorrhages Tomography, X-Ray Computed Neural Networks, Computer Deep Learning
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