Columns are genes, miRNAs, drugs, or cnv. Rows are patient identifiers or cell lines.

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194 Views

It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project. 

In addition, we provide synthetic data for a mixture of isotropic distributions.

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194 Views

The dermoscopic images considered in the paper "Dermoscopic Image Classification with Neural Style Transfer" are available for public download through the ISIC database (https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main). These are 24-bit JPEG images with a typical resolution of 768 × 512 pixels. However, not all the images in the database are in satisfactory condition.

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217 Views

This dataset has information of 83 patients from India. This dataset contains patients’ clinical history, histopathological features, and mammogram. The distinctive aspect of this dataset lies in its collection of mammograms that have benign tumors and used in subclassification of benign tumors. 

Instructions: 

This datasest contains a zip folder of 80 mammograms and an excel file having mammographic features, histopathological features as well as clinical fatures of all the patients. 

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532 Views

Of late, efforts are underway to build computer-assisted diagnostic tools for cancer diagnosis via image processing. Such computer-assisted tools require capturing of images, stain color normalization of images, segmentation of cells of interest, and classification to count malignant versus healthy cells. This dataset is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer. The images are provided after stain color normalization.

Instructions: 

IMPORTANT:

If you use this dataset, please cite below publications-

  1. Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images," Medical Image Analysis, vol. 65, Oct 2020. DOI: https://doi.org/10.1016/j.media.2020.101788. (2020 IF: 11.148)
  2. Shiv Gehlot, Anubha Gupta and Ritu Gupta, "EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 1389-1393.
  3. Anubha Gupta, Pramit Mallick, Ojaswa Sharma, Ritu Gupta, and Rahul Duggal, "PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma," PLoS ONE 13(12): e0207908, Dec 2018. DOI: 10.1371/journal.pone.0207908
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1015 Views

Dr.

Simulated data: dual-polarized antenna array in GNSS

Instructions: 

Cover letter

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12 Views

From manuscript: Lymph Node Ratio after Neoadjuvant Chemotherapy for Stage II/III Breast Cancer: Prognostic Value Measured with Gini’s Mean Difference of Restricted Mean Survival Times.

Bhumsuk Keam, Olena Gorobets, Vincent Vinh-Hung, Seock-Ah Im.

https://doi.org/10.1177/11769351211051675

Instructions: 

Breast cancer Neoadjuvant chemotherapy

1 header row.

370 data rows

columns = characteristics, refer to papers for detailed description

 

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160 Views

 Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images.

Instructions: 

In order to load the data, we provide below an example routine working within PyTorch frameworks. We provide two different resolutions, 800 and 7000 um/px.

Within each resolution, we provide .csv files, containing all metadata information for all the included files, comprising:

  • image_id;
  • label (6 classes - HP, NORM, TA.HG, TA.LG, TVA.HG, TVA.LG);
  • type (4 classes - HP, NORM, HG, LG);
  • reference WSI;
  • reference region of interest in WSI (roi);
  • resolution (micron per pixels, mpp);
  • coordinates for the patch (x, y, w, h).

Below you can find the dataloader class of UNITOPatho for PyTorch. More examples can be found here.


import torch

import torchvision

import numpy as np

import cv2

import os

 

class UNITOPatho(torch.utils.data.Dataset):

def __init__(self, df, T, path, target, subsample=-1, gray=False, mock=False):

self.path = path

self.df = df

self.T = T

self.target = target

self.subsample = subsample

self.mock = mock

self.gray = gray

allowed_target = ['type', 'grade', 'top_label']

if target not in allowed_target:

print(f'Target must be in {allowed_target}, got {target}')

exit(1)

print(f'Loaded {len(self.df)} images')
 

def __len__(self):

return len(self.df)

def __getitem__(self, index):

entry = self.df.iloc[index]

image_id = entry.image_id

image_id = os.path.join(self.path, entry.top_label_name, image_id)

img = None

if self.mock:

C = 1 if self.gray else 3

img = np.random.randint(0, 255, (224, 224, C)).astype(np.uint8)

else:

img = cv2.imread(image_id)

if self.subsample != -1:

w = img.shape[0]

while w//2 > self.subsample:

img = cv2.resize(img, (w//2, w//2))

w = w//2

img = cv2.resize(img, (self.subsample, self.subsample))

if self.gray:

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

img = np.expand_dims(img, axis=2)

else:

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

if self.T is not None:

img = self.T(img)

return img, entry[self.target]

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1015 Views

This dataset is in support of my 2 Research Papers.  This dataset is in support of my research paper     " initially submitted to IEEE 

Paper Abstract

Instructions: 

Read Me

(1) This is an open access ,so everything  can be downloaded after login (free signup). You have to click on 'Title'.

(2) The folder 'Experimental' has recordings of Magnetic fields measured using  Magnetic Sensor,  mobile app(software) and mobile phone

  • Physical Magnetic Sensor(hardware)

                  Resolution of the sensor is 0.0976 uT

                  Maximum Range of the sensor : 3000.0044 uT

  • Physical orientation and angular velocity  Sensor  (hardware)

                        Resolution of the sensor is 0.0012216975 rad/s

                        Maximum Range of the sensor : 34.90549 rad/s

  •  Physical Proximity Sensor (hardware)

                          Resolution of the sensor : 1.0 cm 

                          Maximum Range of the sensor : 5.0 cm

  •  Physical Gravity Sensor (hardware)

                        Resolution of the sensor :  0.01 m/s2  & Maximum Range of the sensor :156.98999 m/s2

5gproof.zip has screenshots from wifi detection

Experimental Result   -    Lowest Recorded Reading : 11 uT

Highest Recorded Reading : 479 uT

 

Around 300 uT can be measured anywhere, if nearby has 5G equipment ( fluctuates to 50 uT then 111, then 200 , 286,  ...)    .   

Reading of 479 was measured, as few people were feeling unwell and when I checked, it was 420 uT, stationary and fluctuating to it around but that is not recorded.   So after some time, this was recorded.

 

 

(3) Zip do not contain simulation project folder.

(4)  The model used is given in  https://dx.doi.org/10.21227/9ab4-tv57.  But for this study, extra circuits has been added, details are given in the Paper.These are very elemenetary & easy for any degree holder,so not given.The parameters of 4g and 5g is in the paper ' '   . Pls contact university professor if any doubt.

(5) For  details like model block diagram, parameters, analysis, interpretation, mathematical formulae used to obtain these results etc. please refer "Transaction Paper".

(4) The folder Simulation_  is the data   of and has 8 subfolders

 

(3) The folder 'Simulation_4G' has folder

  • Heart
  • Ear
  • Tissue
  • Lungs

(4) The folder 'Simulation_5G' has 3  sub-folders

  • Low
  • Mid
  • High

(5) Each of the subfolder of 5G has folders

Scripts

Script is uploaded on only IEEE Codeocean, pls. follow instructions in the capsule given in ReadMe.txt.  Capsule of IEEE Codeocean (Matlab, MIT License) is:

  • Code:

 

Paper Citing : If want to cite this in paper etc. ,please refer DoI and/or this url.

Funding: There are no funders for this submission. The  author has himself fully self-financed.

Acknowlegement :  The author as such thankful to none and does not have any special name to be acknowledged.

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677 Views

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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689 Views

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