AI Medical Compendium Topic:
Tomography, X-Ray Computed

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Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.

Cell reports methods
Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. ...

COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.

PloS one
During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases...

Masked cross-domain self-supervised deep learning framework for photoacoustic computed tomography reconstruction.

Neural networks : the official journal of the International Neural Network Society
Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct PA images with a supervised scheme, which requires high-quality images as ground truth labels. However, p...

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation.

Neural networks : the official journal of the International Neural Network Society
Segmentation and the subsequent quantitative assessment of the target object in computed tomography (CT) images provide valuable information for the analysis of intracerebral hemorrhage (ICH) pathology. However, most existing methods lack a reasonabl...

ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification.

Medical image analysis
State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervise...

Transformers for colorectal cancer segmentation in CT imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying v...

Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-...

Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.

Journal of imaging informatics in medicine
Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenoca...

Prediction of Anastomotic Leakage in Esophageal Cancer Surgery: A Multimodal Machine Learning Model Integrating Imaging and Clinical Data.

Academic radiology
RATIONALE AND OBJECTIVES: Surgery in combination with chemo/radiotherapy is the standard treatment for locally advanced esophageal cancer. Even after the introduction of minimally invasive techniques, esophagectomy carries significant morbidity and m...