AIMC Topic: Image Processing, Computer-Assisted

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Global and Local Feature Reconstruction for Medical Image Segmentation.

IEEE transactions on medical imaging
Learning how to capture long-range dependencies and restore spatial information of down-sampled feature maps are the basis of the encoder-decoder structure networks in medical image segmentation. U-Net based methods use feature fusion to alleviate th...

DeepKeyGen: A Deep Learning-Based Stream Cipher Generator for Medical Image Encryption and Decryption.

IEEE transactions on neural networks and learning systems
The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients' medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream ci...

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).

Computational intelligence and neuroscience
Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearan...

Image Semantic Segmentation of Underwater Garbage with Modified U-Net Architecture Model.

Sensors (Basel, Switzerland)
Autonomous underwater garbage grasping and collection pose a great challenge to underwater robots. To assist underwater robots in locating and recognizing underwater garbage objects efficiently, a modified U-Net-based architecture consisting of a dee...

BRefine: Achieving High-Quality Instance Segmentation.

Sensors (Basel, Switzerland)
Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone ...

Enhancing MR image segmentation with realistic adversarial data augmentation.

Medical image analysis
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impra...

Application of Convolutional Neural Network in Motor Bearing Fault Diagnosis.

Computational intelligence and neuroscience
In the field of mechanical and electrical equipment, the motor rolling bearing is a workpiece that is extremely prone to damage and failure. However, the traditional fault diagnosis methods cannot keep up with the development pace of the times becaus...

A data augmentation method for fully automatic brain tumor segmentation.

Computers in biology and medicine
Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net archit...

C -GAN: Content-consistent generative adversarial networks for unsupervised domain adaptation in medical image segmentation.

Medical physics
PURPOSE: In clinical practice, medical image analysis has played a key role in disease diagnosis. One of the important steps is to perform an accurate organ or tissue segmentation for assisting medical professionals in making correct diagnoses. Despi...

KaIDA: a modular tool for assisting image annotation in deep learning.

Journal of integrative bioinformatics
Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehens...