AIMC Topic: Deep Learning

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Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

Environmental science and pollution research international
The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and ...

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study.

BMC oral health
BACKGROUND: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x a...

Joint reconstruction and segmentation in undersampled 3D knee MRI combining shape knowledge and deep learning.

Physics in medicine and biology
Task-adapted image reconstruction methods using end-to-end trainable neural networks (NNs) have been proposed to optimize reconstruction for subsequent processing tasks, such as segmentation. However, their training typically requires considerable ha...

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) statu...

Classification Method of ECG Signals Based on RANet.

Cardiovascular engineering and technology
BACKGROUND: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias.

Fast SPECT/CT planar bone imaging enabled by deep learning enhancement.

Medical physics
BACKGROUND: The application of deep learning methods in rapid bone scintigraphy is increasingly promising for minimizing the duration of SPECT examinations. Recent works showed several deep learning models based on simulated data for the synthesis of...

Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.

Medical & biological engineering & computing
Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermo...

Application of a novel deep learning-based 3D videography workflow to bat flight.

Annals of the New York Academy of Sciences
Studying the detailed biomechanics of flying animals requires accurate three-dimensional coordinates for key anatomical landmarks. Traditionally, this relies on manually digitizing animal videos, a labor-intensive task that scales poorly with increas...

Pixelated High- Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics.

ACS nano
Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp reso...

Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS.

Abdominal radiology (New York)
PURPOSE: To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical ...