AIMC Topic: Image Processing, Computer-Assisted

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Research on Multicamera Photography Image Art in BERT Motion Based on Deep Learning Mode.

Computational intelligence and neuroscience
In order to improve the artistic expression effect of photographic images, this article combines the deep learning model to conduct multicamera photographic image art research in BERT motion. Moreover, this article analyzes the external parameter err...

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation.

Computers in biology and medicine
Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep le...

Research on Vehicle Lane Change Warning Method Based on Deep Learning Image Processing.

Sensors (Basel, Switzerland)
In order to improve vehicle driving safety in a low-cost manner, we used a monocular camera to study a lane-changing warning algorithm for highway vehicles based on deep learning image processing technology. We improved the mask region-based convolut...

Research on Rice Yield Prediction Model Based on Deep Learning.

Computational intelligence and neuroscience
Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people'...

Swarm learning for decentralized artificial intelligence in cancer histopathology.

Nature medicine
Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obsta...

Phase retrieval based on deep learning in grating interferometer.

Scientific reports
Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is di...

Clinical target segmentation using a novel deep neural network: double attention Res-U-Net.

Scientific reports
We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest obje...

Dense Convolutional Network and Its Application in Medical Image Analysis.

BioMed research international
Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of De...

Explainability of deep neural networks for MRI analysis of brain tumors.

International journal of computer assisted radiology and surgery
PURPOSE: Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restrictio...

Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.

Sensors (Basel, Switzerland)
In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context...