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Image Processing, Computer-Assisted - AI Medical Compendium

AIMC Topic: Image Processing, Computer-Assisted

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Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images.

Sensors (Basel, Switzerland)
In recent years, the use of deep learning-based models for developing advanced healthcare systems has been growing due to the results they can achieve. However, the majority of the proposed deep learning-models largely use convolutional and pooling o...

Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy.

Journal of applied clinical medical physics
PURPOSE: Dose computation using cone beam computed tomography (CBCT) images is inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to assess the dosimetric accuracy of synthetic computed tomography (CT)-based cal...

Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity.

NeuroImage
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We...

A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.

Medical & biological engineering & computing
Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip fracture risk. In this study, we proposed an approach based on deep learning for the fast and automat...

Dual-Path Residual "Shrinkage" Network for Side-Scan Sonar Image Classification.

Computational intelligence and neuroscience
The underwater environment is complicated and changeable and contains many noises, making it difficult to detect a particular object in the underwater environment. At present, the main seabed detection technology explores the seabed environment with ...

Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Prostate cancer is the most common cancer of the male reproductive system. With the development of medical imaging technology, magnetic resonance images (MRI) have been used in the diagnosis and treatment of prostate cancer ...

General Image Fusion for an Arbitrary Number of Inputs Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
In this paper, we propose a unified and flexible framework for general image fusion tasks, including multi-exposure image fusion, multi-focus image fusion, infrared/visible image fusion, and multi-modality medical image fusion. Unlike other deep lear...

Bayesian machine learning analysis of single-molecule fluorescence colocalization images.

eLife
Multi-wavelength single-molecule fluorescence colocalization (CoSMoS) methods allow elucidation of complex biochemical reaction mechanisms. However, analysis of CoSMoS data is intrinsically challenging because of low image signal-to-noise ratios, non...

Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN).

Computational intelligence and neuroscience
The lack of annotated datasets makes the automatic detection of skin problems very difficult, which is also the case for most other medical applications. The outstanding results achieved by deep learning techniques in developing such applications hav...

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm.

Contrast media & molecular imaging
This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologic...