Medical Imaging
This dataset contains 1216 data, which are scanned by HIS-RING PACT system.
the data sampling rate of our system is 40 MSa/s, a 128-elements 2.5MHz full-view ring-shaped transducer with 30mm radius.
continuous updating.....
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Diabetic Retinopathy is the second largest cause of blindness in diabetic patients. Early diagnosis or screening can prevent the visual loss. Nowadays , several computer aided algorithms have been developed to detect the early signs of Diabetic Retinopathy ie., Microaneurysms. The AGAR300 dataset presented here facilitate the researchers for benchmarking MA detection algorithms using digital fundus images. Currently, we have released the first set of database which consists of 28 color fundus images, shows the signs of Microaneurysm.
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See our next articles.
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The dataset is genrated by the fusion of three publicly available datasets: COVID-19 cxr image (https://github.com/ieee8023/covid-chestxray-dataset), Radiological Society of North America (RSNA) (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge), and U.S. national library of medicine (USNLM) collected Montgomery country - NLM(MC) (http
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We chose 8 publicly available CT volumes of COVID-19 positive patients which were available from https://doi.org/10.5281/zenodo.3757476 and used 3D slicer to generate volumetric annotations of 512*512 dimension for 5 lung lobes namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe. These annotations are validated by a radiologist with over 15 years of experience.
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This is a dataset of diabetic foot. We are preparing to publish this dataset.
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The data uploaded here shall support the paper
Decision Tree Analysis of ...
which has been submitted to IEEE Transactions on Medical Imaging (2020, September 25) by the authors
Julian Mattes, Wolfgang Fenz, Stefan Thumfart, Gerhard Haitchi, Pierre Schmit, Franz A. Fellner
During review the data shall only be visible for the reviewers of this paper. Afterwards this abstract will be modified and complemented and a dataset image will be uploaded.
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Microscopic image based analysis plays an important role in histopathological computer based diagnostics. Identification of childhood medulloblastoma and its proper subtype from biopsy tissue specimen of childhood tumor is an integral part for prognosis.The dataset is of Childhood medulloblastoma (CMB) biopsy samples. The images are of 10x and 100x microscopic magnifications, uploaded in separate folders. The images consist of normal brain tissue cell samples and CMB cell samples of different WHO defined subtypes. An excel sheet is also uploaded for ease of data description.
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2100 Views
70 images with a total of more than 2 GB of data have been employed for the experimental evaluation. All images are acquired at 24 bpp with 8 bits per pixel per component (bpppc). They depict various tissues of different sizes and stained with Hematoxylin and Eosin (H\&E) stain. The tissues employed in the experiments are skin fibroblast (SKNF), endometrial (END), lung (LNGF), embryonic stem cells (ES), kidney clear cell carcinoma (KIRC), pancreas (PANC), brain glioblastoma multiforme (GBM), colon adenocarcinoma (COAD), and lymphatic (LYMP).
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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.
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