AIMC Topic: Neural Networks, Computer

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Image Compositing for Segmentation of Surgical Tools Without Manual Annotations.

IEEE transactions on medical imaging
Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This is often a rate-limiting factor when large amounts of labeled images are required, such as for training deep convolutional networks for instrument-backgro...

Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition.

IEEE transactions on medical imaging
The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in ...

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

IEEE transactions on medical imaging
Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually a...

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Computational and mathematical methods in medicine
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique r...

Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level.

Food chemistry
Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. First...

Promotion time cure rate model with a neural network estimated nonparametric component.

Statistics in medicine
Promotion time cure rate models (PCM) are often used to model the survival data with a cure fraction. Medical images or biomarkers derived from medical images can be the key predictors in survival models. However, incorporating images in the PCM is c...

Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method.

Computers in biology and medicine
Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insuffici...

Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network.

Sensors (Basel, Switzerland)
Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of t...

Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.

Scientific reports
This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convoluti...

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning.

Scientific data
Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a ti...