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.

Instructions: 

The dataset contains two folder of diffrent magnification images, i.e; 10x and 100x. The type of each image is described in the provided excel file. Each slide has a unique number and the number in bracket denotes that the corresponding image is of the single slide. 

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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.

Instructions: 

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

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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.

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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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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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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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. 

Instructions: 

UWF FP images, UWF FA images, labeled UWF FP vessel maps, and binary UWF FP validity masks are provided, where the file names indicate the correspondence among them.

 

Users of the dataset should cite the following paper

L. Ding, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff, and G. Sharma, ``Weakly-supervised vessel detection in ultra-widefield fundus photography via iterative multi-modal registration and learning,'' IEEE Trans. Medical Imaging, accepted for publication, to appear.

 

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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.

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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