AIMC Topic: Image Processing, Computer-Assisted

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A Deep Learning Framework for Skull Stripping in Brain MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Skull-stripping, an important pre-processing step in neuroimage computing, involves the automated removal of non-brain anatomy (such as the skull, eyes, and ears) from brain images to facilitate brain segmentation and analysis. Manual segmentation is...

Estimation of Wound Area and Severity Level of Skin tear using Deep Learning Methods.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Skin tears occur mainly in older adults, making it difficult to identify the wound area and severity level when making care decision. We propose an algorithm for estimating the wound area and severity level of skin tears using a deep learning method....

A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose a neural network-based framework to optimize the perceptions simulated by the in silico retinal implant model pulse2percept. The overall pipeline consists of a trainable encoder, a pre-trained retinal implant model and a pre-trained evalua...

A novel and simple approach to regularise attention frameworks and its efficacy in segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep neural networks with attention mechanism have shown promising results in many computer vision and medical image processing applications. Attention mechanisms help to capture long range interactions. Recently, more sophisticated attention mechani...

AI Techniques to Identify Nerve Cell Alterations in Digital Images.

Studies in health technology and informatics
Artificial intelligence (AI), utilising computing power, has managed to influence the health sector with many applications based on algorithms, tools, and automated processes. In this work, neuronbiological images acquired by an electronic microscope...

[Research status and outlook of deep learning in oral and maxillofacial medical imaging].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
Artificial intelligence, represented by deep learning, has received increasing attention in the field of oral and maxillofacial medical imaging, which has been widely studied in image analysis and image quality improvement. This narrative review prov...

[Metal artifact reduction and clinical verification in oral and maxillofacial region based on deep learning].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
To construct a kind of neural network for eliminating the metal artifacts in CT images by training the generative adversarial networks (GAN) model, so as to provide reference for clinical practice. The CT data of patients treated in the Department ...

Effect of learning parameters on the performance of the U-Net architecture for cell nuclei segmentation from microscopic cell images.

Microscopy (Oxford, England)
Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesi...

ENRICHing medical imaging training sets enables more efficient machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In...