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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711 Views

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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1276 Views

Giemsa-stained thin blood smear slides from 150 P. falciparum-infected and 50 healthy patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. The smartphone’s built-in camera acquired images of slides for each microscopic field of view.

Instructions: 

Five folders. Parasitized, uninfected, bad segmentation, unsure, weird. 

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148 Views

Model .h5 files and .pb files for robustly detecting glaucoma from optical coherence tomography (OCT) images and for interpretability analysis via testing with concept activation vectors (TCAVs by Been Kim et al.). Further details described in paper "Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in OCT Images" in preparation/under review.

Instructions: 

Included are four .h5 files corresponding to the end-to-end deep learning OCT-fine-tuned Convolutional Neural Networks described in our paper. These models can be loaded using Keras and applied to detect glaucoma in OCT images (retinal nerve fiber layer probability maps). Also included are the .h5, .pb, and .txt files corresponding to the InceptionV3 + FC model and OCT-concept labels used for interpretability analysis via TCAVs as described in our paper. This model can be used via the wrapper class implemented on our forked TCAV repository included in the ‘Run TCAV’ jupyter notebooks located in our GitHub repository: http://github.com/LIINC/TCAV4OCT under src/TCAVRandomConcepts10 and under src/TCAVRandomConcepts160.

 

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85 Views

 

Example axial and coronal phase maps and post-treatment MRI from 68 thalamotomies in essential tremor patients and four pallidotomies in Parkinson's disease patients. From the manuscript "Using phase data from MR temperature imaging to visualize anatomy during MRI-guided focused ultrasound neurosurgery" published in 2020 in IEEE Trans. Med. Imaging.

 

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Instructions: 

Open in PDF viewer

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57 Views

This dataset contains the trained model that accompanies the publication of the same name:

 Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors

 

Instructions: 

The dataset contains 3 parts:

  • Pre-processing: Script to extract brain volume from surrounding skull in non-contrast computed tomography (NCCT) scans and instructions for further pre-processing.
  • Trained convolutional neural network (CNN) to perform automated segmentations
  • Post-processing script to improve CNN-based segmentations

 

Independent Instructions for each part are also contained within each folder.

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239 Views

PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield fundus photography. PRIME-FP20 provides 15 high-resolution ultra-widefield fundus photography images acquired using the Optos 200Tx camera (Optos plc, Dunfermline, United Kingdom), the corresponding labeled binary vessel maps, and the corresponding binary masks for the FOV of the images.

Instructions: 

Ultra-widefield fundus photography images and the corresponding labeled vessel maps and binary masks are provided where the file names indicate the correspondence between them.

Currently, only a sample low-resolution image is provided. The full set of high-resolution images will be provided upon the publication of the associated paper, which is currently submitted for review.

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200 Views

This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training.

Instructions: 

This tool model propose a Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT architecture. Based on autoencoder of Mask-RCNN for area mark feature maps objection detection for the identification of COVID-19 pneumonia have very serious pathological and always accompanied by various of symptoms. We collect a lot of lung x-ray images were be integrated into DICM style dataset prepare for experiment on computer on vision algorithms, and deep learning architecture based on autoencoder of Mask- RCNN algorithms are the main technological breakthrough.

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1448 Views

Knee Magnetic Resonance Images

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49 Views

The outbreak of COVID-19 in Wuhan, China in December 2019 has rapidly spread across other countries in the world and has been declared as a global pandemic by WHO on 11th March, 2020. COVID-19 continues to have adverse effects on the health and economy of the global population and has brought immense pressure on the health care systems of the developing as well as developed countries.

Instructions: 

Please refer the "Readme_CXR_Database_v1.0" for detailed instructions on how to use the dataset. 

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1856 Views

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