AIMC Topic: Image Processing, Computer-Assisted

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Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.

IEEE journal of biomedical and health informatics
Perihematomal edema (PHE) volume, surrounding spontaneous intracerebral hemorrhage (SICH), is an important biomarker for the presence of SICH-associated diseases. However, due to irregular shapes and extremely low contrast of PHE on CT images, manual...

AL-Net: Attention Learning Network Based on Multi-Task Learning for Cervical Nucleus Segmentation.

IEEE journal of biomedical and health informatics
Cervical nucleus segmentation is a crucial and challenging issue in automatic pathological diagnosis due to uneven staining, blurry boundaries, and adherent or overlapping nuclei in nucleus images. To overcome the limitation of current methods, we pr...

Unsupervised Cross-Modality Domain Adaptation Network for X-Ray to CT Registration.

IEEE journal of biomedical and health informatics
2D/3D registration that achieves high accuracy and real-time computation is one of the enabling technologies for radiotherapy and image-guided surgeries. Recently, the Convolutional Neural Network (CNN) has been explored to significantly improve the ...

Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model.

Computational intelligence and neuroscience
In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the curr...

Optimization algorithm of CT image edge segmentation using improved convolution neural network.

PloS one
To address the problem of high failure rate and low accuracy in computed tomography (CT) image edge segmentation, we proposed a CT sequence image edge segmentation optimization algorithm using improved convolution neural network. Firstly, the pattern...

⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning.

Medical image analysis
Convolutional neural networks (CNNs) are increasingly adopted in medical imaging, e.g., to reconstruct high-quality images from undersampled magnetic resonance imaging (MRI) acquisitions or estimate subject motion during an examination. MRI is natura...

NPBDREG: Uncertainty assessment in diffeomorphic brain MRI registration using a non-parametric Bayesian deep-learning based approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Quantification of uncertainty in deep-neural-networks (DNN) based image registration algorithms plays a critical role in the deployment of image registration algorithms for clinical applications such as surgical planning, intraoperative guidance, and...

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction.

Biomolecules
The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence st...

A Deep Learning-Based Piano Music Notation Recognition Method.

Computational intelligence and neuroscience
In the era of rapid development of computer technology, piano music notation and electronic synthesis system can be established using computer technology, and the basic laws of music score can be analyzed from the perspective of image processing, whi...

Accelerating multi-echo chemical shift encoded water-fat MRI using model-guided deep learning.

Magnetic resonance in medicine
PURPOSE: To accelerate chemical shift encoded (CSE) water-fat imaging by applying a model-guided deep learning water-fat separation (MGDL-WF) framework to the undersampled k-space data.