In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably grapple wi...
BACKGROUND AND PURPOSE: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy.
OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrh...
BACKGROUND: Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, esp...
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. ...
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...
Neural networks : the official journal of the International Neural Network Society
Jul 7, 2024
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...
Neural networks : the official journal of the International Neural Network Society
Jul 4, 2024
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...
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...
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