AIMC Topic: Deep Learning

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Predicting Prostate Cancer Molecular Subtype With Deep Learning on Histopathologic Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Microscopic examination of prostate cancer has failed to reveal a reproducible association between molecular and morphologic features. However, deep-learning algorithms trained on hematoxylin and eosin (H&E)-stained whole slide images (WSI) may outpe...

Review on deep learning fetal brain segmentation from Magnetic Resonance images.

Artificial intelligence in medicine
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging ...

SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation.

Computers in biology and medicine
Retinal vessel segmentation is an important task in medical image analysis and has a variety of applications in the diagnosis and treatment of retinal diseases. In this paper, we propose SegR-Net, a deep learning framework for robust retinal vessel s...

Effect of Deep Learning Reconstruction on Evaluating Cervical Spinal Canal Stenosis With Computed Tomography.

Journal of computer assisted tomography
OBJECTIVE: Magnetic resonance imaging (MRI) is commonly used to evaluate cervical spinal canal stenosis; however, some patients are ineligible for MRI. We aimed to assess the effect of deep learning reconstruction (DLR) in evaluating cervical spinal ...

A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging.

Journal of computer assisted tomography
OBJECTIVES: Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis.

Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques.

Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft
BACKGROUND: Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head position, which reduces the accuracy of cephalometric measurements, can be challenging to detect for c...

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.

Computers in biology and medicine
The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational ...

Measurement of interspinous motion in dynamic cervical radiographs using a deep learning-based segmentation model.

Journal of neurosurgery. Spine
OBJECTIVE: Interspinous motion (ISM) is a representative method for evaluating the functional fusion status following anterior cervical discectomy and fusion (ACDF) surgery, but the associated measuring difficulty and potential errors in the clinical...

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images.

Chemosphere
Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their respective advantages in soil carbon content prediction, and the effective fusion of VNIR and HSI is of great significance for improving the prediction ac...

A survey on deep learning for skin lesion segmentation.

Medical image analysis
Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of ...