AIMC Topic: Tomography, X-Ray Computed

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CT-Less Whole-Body Bone Segmentation of PET Images Using a Multimodal Deep Learning Network.

IEEE journal of biomedical and health informatics Feb 10, 2025
In bone cancer imaging, positron emission tomography (PET) is ideal for the diagnosis and staging of bone cancers due to its high sensitivity to malignant tumors. The diagnosis of bone cancer requires tumor analysis and localization, where accurate a...
Whole Body Imaging Bone Neoplasms Positron-Emission Tomography Humans Bone and Bones Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted
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One-shot learning for generalization in medical image classification across modalities.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Feb 9, 2025
Generalizability is one of the biggest challenges hindering the advancement of medical sensing technologies across multiple imaging modalities. This issue is further impaired when the imaging data is limited in scope or of poor quality. To tackle thi...
Image Processing, Computer-Assisted Algorithms Diagnostic Imaging Humans Tomography, X-Ray Computed Machine Learning Deep Learning
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DKCN-Net: Deep kronecker convolutional neural network-based lung disease detection with federated learning.

Computational biology and chemistry Feb 8, 2025
In the healthcare field, lung disease detection techniques based on deep learning (DL) are widely used. However, achieving high stability while maintaining privacy remains a challenge. To address this, this research employs Federated Learning (FL), e...
Federated Learning Deep Learning Convolutional Neural Networks Lung Diseases Humans Neural Networks, Computer Tomography, X-Ray Computed
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Learning robust medical image segmentation from multi-source annotations.

Medical image analysis Feb 8, 2025
Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. However, learning segmentation networks from multi-sourc...
Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Machine Learning Algorithms
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Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Feb 8, 2025
PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region.
Head and Neck Neoplasms Magnetic Resonance Imaging Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Female Aged Male Middle Aged Deep Learning Humans
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Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning.

Computers in biology and medicine Feb 8, 2025
Infected region segmentation is crucial for pulmonary infection diagnosis, severity assessment, and monitoring treatment progression. High-performance segmentation methods rely heavily on fully annotated, large-scale training datasets. However, manua...
Humans Tomography, X-Ray Computed Lung Deep Learning Imaging, Three-Dimensional
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A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.

Current oncology (Toronto, Ont.) Feb 8, 2025
BACKGROUND: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management.
Humans Algorithms Lung Neoplasms Multiple Pulmonary Nodules Tomography, X-Ray Computed Solitary Pulmonary Nodule Female Deep Learning
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Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons Feb 7, 2025
Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well underst...
Risk Factors Adult Male Retrospective Studies Tomography, X-Ray Computed Female Humans Follow-Up Studies Machine Learning Lung Transplantation Prognosis Primary Graft Dysfunction Middle Aged Tissue Donors Graft Survival
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A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal Cancer.

Academic radiology Feb 7, 2025
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography-based radiomics nomogram for cancer-specific survival (CSS) prediction in curatively resected colorectal cancer (CRC), and its performance was compared with the American Joint Co...
Machine Learning Prospective Studies Prognosis Humans Middle Aged Colorectal Neoplasms Nomograms Tomography, X-Ray Computed Radiomics Neoplasm Staging Female Male Aged
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Integration of radiomic and deep features to reliably differentiate benign renal lesions from renal cell carcinoma.

European journal of radiology Feb 7, 2025
PURPOSE: Accurate differentiation of benign renal lesions from renal cell carcinoma (RCC) is crucial for optimized management, particularly for small renal lesions (≤4 cm in diameter). This study aimed to integrate clinical data, radiomic features, a...
Humans Retrospective Studies Tomography, X-Ray Computed Female Middle Aged Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Reproducibility of Results Aged Deep Learning Kidney Neoplasms Radiomics Aged, 80 and over Carcinoma, Renal Cell Adult Diagnosis, Differential Male
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