AIMC Topic: Deep Learning

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An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.

Abdominal radiology (New York)
An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is...

Noise-resistant sharpness-aware minimization in deep learning.

Neural networks : the official journal of the International Neural Network Society
Sharpness-aware minimization (SAM) aims to enhance model generalization by minimizing the sharpness of the loss function landscape, leading to a robust model performance. To protect sensitive information and enhance privacy, prevailing approaches add...

Deep learning corrects artifacts in RASER MRI profiles.

Magnetic resonance imaging
A newly developed magnetic resonance imaging (MRI) approach is based on "Radiowave amplification by the stimulated emission of radiation" (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require...

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics.

Medical physics
BACKGROUND: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.

Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. Accumulated studies have demonstrated the bright prospect of deep learning in processing medical imaging. In this study, we perform a systematic revie...

Deep learning approaches for classification of copper-containing metal scrap in recycling processes.

Waste management (New York, N.Y.)
Separating copper from iron scrap is a critical operation in metal recycling and achieving this with low cost sensoric equipment like RGB cameras instead of XRF/XRT is becoming increasingly attractive. In this article, the groundwork for creating an ...

Robust brain MRI image classification with SIBOW-SVM.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention an...

Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

European journal of radiology
BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).

Detection of carotid plaques on panoramic radiographs using deep learning.

Journal of dentistry
OBJECTIVES: Panoramic radiographs (PRs) can reveal an incidental finding of atherosclerosis, or carotid artery calcification (CAC), in 3-15% of examined patients. However, limited training in identification of such calcifications among dental profess...