AIMC Topic: Image Processing, Computer-Assisted

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Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network.

Journal of healthcare engineering
One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine l...

A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging.

Nature communications
In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true...

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet.

Journal of healthcare engineering
Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th most commonly occurring cancer among the other cancers in the human society, such that about 300,000 ne...

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning.

NeuroImage
The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and...

"Virtual" attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely used for coronary artery disease (CAD) evaluation. Although attenuation correction is recommended to diminish image artifacts and improve d...

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.

European journal of nuclear medicine and molecular imaging
Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancemen...

Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Total-body dynamic positron emission tomography/computed tomography (PET/CT) provides much sensitivity for clinical imaging and research, bringing new opportunities and challenges regarding the generation of total-body parametric images. Thi...

Image quality assessment for machine learning tasks using meta-reinforcement learning.

Medical image analysis
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-bas...

Implicit data crimes: Machine learning bias arising from misuse of public data.

Proceedings of the National Academy of Sciences of the United States of America
SignificancePublic databases are an important resource for machine learning research, but their growing availability sometimes leads to "off-label" usage, where data published for one task are used for another. This work reveals that such off-label u...

An end-to-end computer vision methodology for quantitative metallography.

Scientific reports
Metallography is crucial for a proper assessment of material properties. It mainly involves investigating the spatial distribution of grains and the occurrence and characteristics of inclusions or precipitates. This work presents a holistic few-shot ...