AIMC Topic: Image Processing, Computer-Assisted

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Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining.

Scientific reports
To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unlabeled cell culture images captured by a light microscope as input. In this study, A549, HEK293, and ...

Context-Unsupervised Adversarial Network for Video Sensors.

Sensors (Basel, Switzerland)
Foreground object segmentation is a crucial first step for surveillance systems based on networks of video sensors. This problem in the context of dynamic scenes has been widely explored in the last two decades, but it still has open research questio...

Finding a Suitable Class Distribution for Building Histological Images Datasets Used in Deep Model Training-The Case of Cancer Detection.

Journal of digital imaging
The class distribution of a training dataset is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be cost...

Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework.

Clinical nuclear medicine
PURPOSE: The generalizability and trustworthiness of deep learning (DL)-based algorithms depend on the size and heterogeneity of training datasets. However, because of patient privacy concerns and ethical and legal issues, sharing medical images betw...

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning.

Sensors (Basel, Switzerland)
Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tissues and bones overlap. Therefore, various image processing and rib segmentation methods have been pr...

Tri-view two-photon microscopic image registration and deblurring with convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Two-photon fluorescence microscopy has enabled the three-dimensional (3D) neural imaging of deep cortical regions. While it can capture the detailed neural structures in the x-y image space, the image quality along the depth direction is lower becaus...

Deep learning-based automatic segmentation of images in cardiac radiography: A promising challenge.

Computer methods and programs in biomedicine
BACKGROUND: Due to the advancement of medical imaging and computer technology, machine intelligence to analyze clinical image data increases the probability of disease prevention and successful treatment. When diagnosing and detecting heart disease, ...

ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide a safe environment for deep learning users to ...

LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing.

Sensors (Basel, Switzerland)
Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the d...

Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN).

Diagnostic pathology
BACKGROUND: Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI).