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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This study presented six datasets for DNA/RNA sequence alignment for one of the most common alignment algorithms, namely, the Needleman–Wunsch (NW) algorithm. This research proposed a fast and parallel implementation of the NW algorithm by using machine learning techniques. This study is an extension and improved version of our previous work . The current implementation achieves 99.7% accuracy using a multilayer perceptron with ADAM optimizer and up to 2912 giga cell updates per second on two real DNA sequences with a of length 4.1 M nucleotides.

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64 Views

Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions.Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures.

Instructions: 

Generated data, input data and saved models for the publication
Taseef Rahman, Yuanqi Du, Liang Zhao, and Amarda Shehu. Generative Adversarial Learning of Protein Tertiary Structures. Molecules, 2021.
is made available. Instructions accompany the data in a ReadMe.txt in each folder respectively for the ease of use.

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90 Views

This dataset consists of 16 tables of measurements of the evolution of the two arms of women who underwent a mastectomy of one breast at the University Hospitals of Strasbourg (HUS) between 2011 and 2019, and who presented a lymphedema. The measurements correspond to repeated averages of perimeters (in cm) of the arms in different positions from the shoulder (indicated by a number in cm).

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28 Views

The purpose of this data collection was for the validation of a cuffless blood pressure estimation model during activities of daily living. Data were collected on five young healthy individuals (four males, age 28 ± 6.6 yrs) of varied fitness levels, ranging from sedentary to regularly active, and free of cardiovascular and peripheral vascular disease. Arterial blood pressure was continuously measured using finger PPG (Portapres; Finapres Medical Systems, the Netherlands).

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218 Views

Magnetic guidance of cochlear implants is a promising technique to reduce the risk of physical trauma during surgery. In this approach, a magnet attached to the tip of the implant electrode array is guided within the scala tympani using a magnetic field. After surgery, the magnet must be detached from the implant electrode array via localized heating, which may cause thermal trauma, and removed from the scala tympani .

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64 Views

The datasets are related to the findings and results of our investigations of the minimal force thresholds perception in robotic surgical applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system displays grasping tasks by a surgical grasper. The experiments included the display of a set of presentations in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). Sixty participants took part in these experiments and were asked to distinguish between consecutive presentations.

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25 Views

Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.

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187 Views

The PD-BioStampRC21 dataset provides data from a wearable sensor accelerometry studyconducted for studying activity, gait, tremor, and other motor symptoms in individuals with Parkinson's disease (PD).In addition to individuals with PD, the dataset also includes data for controls that also went through the same study protocol as the PD participants. Data were acquired using lightweight MC 10 BioStamp RC sensors (MC 10 Inc, Lexington, MA), five of which were attached to each participant for gathering data over a roughly two day interval.

Instructions: 

Users of the dataset should cite the following paper:

Jamie L. Adams, Karthik Dinesh, Christopher W. Snyder, Mulin Xiong, Christopher G. Tarolli, Saloni Sharma, E. Ray Dorsey, Gaurav Sharma, "A real-world study of wearable sensors in Parkinson’s disease". Submitted.

where an overview of the study protocol is also provided. Additional detail specific to the dataset and file naming conventions is provided here.

The dataset is comprised of two main components: (I) Sensor and UPDRS-assessment-task annotation data for each participant and (II) demographic and clinical assessment data for all participants. Each of these is described in turn below:

I) Sensor and UPDRS-assessment-task annotation data:

For each participant the sensor accelerometry  and UPDRS-assessment-task annotation data are provided as a zip file, for instance, ParticipantID018DataPDBioStampRC.zip for participant ID 018. Unzipping the file generates a folder with a name matching the participant ID, for example, 018, that contains the data organized as the following files. Times and timestamps are consistently reported in units of milliseconds starting from the instant of the earliest sensor recording (for the first sensor applied to the participant).

a) Accelerometer sensor data files (CSV) corresponding to the five different sensor placement locations, which are abbreviated as

   1) Trunk (chest)                  - abbreviated as "ch"

   2) Left anterior thigh           - abbreviated as "ll"

   3) Right anterior thigh        - abbreviated as "rl"

   4) Left anterior forearm      - abbreviated as "lh"

   5) Right anterior forearm    - abbreviated as "rh"

   Example file name for accelerometer sensor data files:

   "AbbreviatedSensorLocation"_ID"ParticipantID"Accel.csv

   E.g. ch_ID018Accel.csv, ll_ID018Accel.csv, rl_ID018Accel.csv, lh_ID018Accel.csv, and rh_ID018Accel.csv

   File format for the accelerometer sensor data files: Comprises of four columns that provide a timestamp for    each measurement and corresponding triaxial accelerometry relative to the sensor coordinate system.

   Column 1: "Timestamp (ms)"             - Time in milliseconds

   Column 2: "Accel X (g)"                      - Acceleration in X-direction (in units of g = 9.8 m/s^2)

   Column 3: "Accel Y (g)"                      - Acceleration in Y-direction (in units of g = 9.8 m/s^2)

   Column 4: "Accel Z (g)"                      - Acceleration in Z-direction (in units of g = 9.8 m/s^2)

b) Annotation file (CSV). This file provides tagging annotations for the sensor data that identify, via start and end timestamps,     the durations of various clinical assessments performed in the study.   

   Example file name for annotation file:

   AnnotID"ParticipantID".csv

   E.g. AnnotID018.csv 

    File format for the annotation file: Comprises of four columns

   Column 1: "Event Type"                      - List of in-clinic MDS-UPDRS assessments. Each assessment comprises of                                                                 two queries -  medication status and MDS-UPDRS assessment body locations

   Column 2: "Start Timestamp (ms)"     - Start timestamp for the MDS-UPDRS assessments

   Column 3: "Stop Timestamp (ms)"      - Stop timestamp for the MDS-UPDRS assessments

   Column 4: "Value"                               - Responses to the queries in Column 1 - medication status (OFF/ON) and                                                                  MDS-UPDRS assessment body locations (E.g. RIGHT HAND, NECK, etc.)

   II) Demographic and clinical assessment data

For all participants, the demographic and clinical assessment data are provided as a zip file "Clinic_DataPDBioStampRCStudy.zip". Unzipping the file generates a CSV file named Clinic_DataPDBioStampRCStudy.csv.

File format for the demographic and clinical assessment data file: Comprises of 19 columns

Column 1: "ID"                                                                              - Participant ID

Column 2: "Sex"                                                                            - Participant sex (Male/Female)

Column 3: "Status"                                                                        - Participant disease status (PD/Control)

Column 4: "Age"                                                                            - Participant age

Column 5: "updrs_3_17a"                                                              - Rest tremor amplitude (RUE - Right Upper Extremity)

Column 6: "updrs_3_17b"                                                              - Rest tremor amplitude (LUE - Left Upper Extremity)

Column 7: "updrs_3_17c"                                                              - Rest tremor amplitude (RLE - Right Lower Extremity)

Column 8: "updrs_3_17d"                                                              - Rest tremor amplitude (LLE - Right Lower Extremity)

Column 9: "updrs_3_17e"                                                              - Rest tremor amplitude (Lip/Jaw)

Column 10 - Column 14: "updrs_3_17a_off" - "updrs_3_17e_off"  - Rest tremor amplitude during OFF medication assessment                                                                                                         (ordering similar as that from Column 5 to Column 9)

Column 15 - Column 19: "updrs_3_17a_on" - "updrs_3_17e_on"   - Rest tremor amplitude during ON medication assessment

For details about different MDS-UPDRS assessments and scoring schemes, the reader is referred to:

Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23, 2129-2170, doi:10.1002/mds.22340 (2008)   

Categories:
278 Views

 

Instructions: 

These .MAT files contain MATLAB Tables of raw and preprocessed data. Information detailing the bed system used to collect these signals and the steps used to create the preprocessed data are contained in a publication in Sensors – Carlson, C.; Turpin, V.-R.; Suliman, A.; Ade, C.; Warren, S.; Thompson, D.E.; Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters. Sensors 2021, 21, 156. https://doi.org/10.3390/s21010156.

The reBAP signal is scaled at 100 mmHg/volt. The interbeat interval (IBI), stroke volume (SV), and dP/dt_max are scaled at 1000 ms/volt, 100 mL/volt, and 1 mHg/s/volt, respectively.

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180 Views

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