AIMC Topic: Deep Learning

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Deep representation learning for clustering longitudinal survival data from electronic health records.

Nature communications
Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities for leveraging machine learning approaches to uncover novel patient subgroups. However, many existing ...

Automatic Detection of Cognitive Impairment in Patients With White Matter Hyperintensity Using Deep Learning and Radiomics.

American journal of Alzheimer's disease and other dementias
White matter hyperintensity (WMH) is associated with cognitive impairment. In this study, 79 patients with WMH from hospital 1 were randomly divided into a training set (62 patients) and an internal validation set (17 patients). In addition, 29 WMH p...

Establishing a Deep Learning Model That Integrates Pretreatment and Midtreatment Computed Tomography to Predict Treatment Response in Non-Small Cell Lung Cancer.

International journal of radiation oncology, biology, physics
PURPOSE: Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiation therapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This st...

CQENet: A segmentation model for nasopharyngeal carcinoma based on confidence quantitative evaluation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of the tumor regions of nasopharyngeal carcinoma (NPC) is of significant importance for radiotherapy of NPC. However, the precision of existing automatic segmentation methods for NPC remains inadequate, primarily manifested in t...

Exploring the significance of the frontal lobe for diagnosis of schizophrenia using explainable artificial intelligence and group level analysis.

Psychiatry research. Neuroimaging
Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted perception of reality. Magnetic resonance imaging (MRI) is an essential tool for diagnosing SZ which helps...

The effect of cryopreservation on enamel microcracks - A μCT analysis using a deep learning algorithm.

Cryobiology
To date, the effect of cryopreservation on microcracks in the dental enamel remains unclear. These enamel microcracks are very thin, at the limit of visibility and their segmentation is beyond the capabilities of traditional image analysis. The objec...

Segment Like A Doctor: Learning reliable clinical thinking and experience for pancreas and pancreatic cancer segmentation.

Medical image analysis
Pancreatic cancer is a lethal invasive tumor with one of the worst prognosis. Accurate and reliable segmentation for pancreas and pancreatic cancer on computerized tomography (CT) images is vital in clinical diagnosis and treatment. Although certain ...

Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data.

Medical image analysis
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain's anatomy, existing...

Deep learning algorithm classification of tympanostomy tube images from a heterogenous pediatric population.

International journal of pediatric otorhinolaryngology
IMPORTANCE: The ability to augment routine post-operative tube check appointments with at-home digital otoscopes and deep learning AI could improve health care access as well as reduce financial and time burden on families.