Medical Imaging
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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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.
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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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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
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The PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield (UWF) fundus photography (FP). PRIME-FP20 provides 15 high-resolution UWF FP 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 valid data region for the images. For each UWF FP image, a concurrently captured UWF fluorescein angiography (FA) is also included.
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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.
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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.
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The following pages show axial T2-weighted MRI obtained at 24 hours and at 3-15 months after MRgFUS. The images shown here were registered to the same reference frame that was used in the thermal simulations; every third image is shown. To segment the bone marrow lesions, the registered images were toggled back and forth between the two time points to detect obvious changes. The lesion segmentations were completed before the acoustic and thermal simulations were performed. They were originally done on the native T2-weighted images acquired at 3-15 months after FUS.
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