AIMC Topic: Deep Learning

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Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection.

PloS one Apr 30, 2024
The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sampl...
Deep Learning Mouth Neoplasms Humans Neural Networks, Computer
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Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

International journal of radiation biology Apr 30, 2024
PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automate...
Dose-Response Relationship, Radiation Neural Networks, Computer Algorithms Humans Deep Learning Poisson Distribution Radiation Dosage Chromosome Aberrations
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Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Small methods Apr 29, 2024
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent...
Deep Learning Humans Metabolomics Machine Learning Metabolome Data Interpretation, Statistical Algorithms
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Choosing the right artificial intelligence solutions for your radiology department: key factors to consider.

Diagnostic and interventional radiology (Ankara, Turkey) Apr 29, 2024
The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide ...
Humans Artificial Intelligence Deep Learning Radiology Radiology Department, Hospital
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Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.

Acta ophthalmologica Apr 29, 2024
PURPOSE: To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and ...
Preoperative Period Recurrence Fluorescein Angiography Visual Acuity Predictive Value of Tests Postoperative Period Vitrectomy Retinal Detachment Scleral Buckling Aged Adult Male Middle Aged Deep Learning Follow-Up Studies Humans Retrospective Studies Female
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Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm.

European radiology Apr 29, 2024
OBJECTIVES: To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans.
Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Female Aged Male Middle Aged Deep Learning Contrast Media Algorithms Humans Retrospective Studies Tomography, X-Ray Computed Artificial Intelligence
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Learning active subspaces and discovering important features with Gaussian radial basis functions neural networks.

Neural networks : the official journal of the International Neural Network Society Apr 29, 2024
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this ...
Neural Networks, Computer Machine Learning Algorithms Humans Deep Learning Normal Distribution
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DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis.

Neural networks : the official journal of the International Neural Network Society Apr 29, 2024
Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper,...
Neural Networks, Computer Artificial Intelligence Machine Learning Humans Diagnosis, Computer-Assisted Algorithms Deep Learning Skin Neoplasms Malaria
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Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 29, 2024
Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the...
Humans Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Magnetic Resonance Imaging Heart Motion
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A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.

Journal of applied clinical medical physics Apr 29, 2024
PURPOSE: To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ...
Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Prognosis Tomography, X-Ray Computed Female Radiotherapy Dosage Radiotherapy, Intensity-Modulated Endometrial Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Brachytherapy Algorithms
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