This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.

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Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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The results of the experiemental work presented in the journal paper with the title : A Novel Visualization of Coronal Views for Macula Vasculature in Optical Coherence Tomography Angiography (OCTA), which has the following abstratct

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This repository contains the data related to the paper “CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging” (10.1109/TUFFC.2021.3131383). It contains multiple datasets used for training and testing, as well as the trained models and results (predictions and metrics). In particular, it contains a large-scale simulated training dataset composed of 31000 images for the three different imaging configuration considered (i.e., low quality, high quality, and ultrahigh quality).

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The detailed description of the data available in this repository can be found online at https://github.com/dperdios/dui-ultrafast/#data.

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 it contains the low-dose CT images used in the experiment.

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Re-curated Breast Imaging Subset DDSM Dataset (RBIS-DDSM) is a curated version of 849 images from the CBIS-DDSM dataset available online with a permissive copyright license (CC-BY-SA 3.0). The  CBIS-DDSM dataset is an improved version of the DDSM dataset. The authors of the CBIS-DDSM dataset attempted to improve the ground truth by applying simple image processing based methods to enhance the edges without any manual intervention from medical experts in order to segment and annotate masses. However, these annotations (segmentation maps) are inaccurate in most of the images. 

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Parasitic infections have been recognised as one of the most significant causes of illnesses by WHO. Most infected persons shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals. Diagnosis of intestinal parasites is usually based on direct examination in the laboratory, of which capacity is obviously limited.

Last Updated On: 
Thu, 02/24/2022 - 15:33
Citation Author(s): 
Duangdao Palasuwan, Thanarat H. Chalidabhongse, Korranat Naruenatthanaset, Thananop Kobchaisawat, Kanyarat Boonpeng, Nuntiporn Nunthanasup, Nantheera Anantrasirichai

Research data associated with paper: A Semantic Segmentation Model for Lumbar MRI Images using Divergence Loss, comprising the python code, a trained model and empirical results. 

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The Femoropopliteal Artery Stent Dataset is captured by Siemens digital radiography fluoroscopy system Luminos dRF Max. The dataset is from the First Affiliated Hospital of Fujian Medical University in Fuzhou City (Fujian Province, China). The X-ray fluoroscopic image data set of the femoropopliteal artery stent was obtained from different positions of the patient.

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Reconstructed 2D Shepp-Logan phantom and brain MRA projected image using different flat portions in pulsed excitation and magnetic nanoparticle sizes.

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