# - * -coding: utf - 8 - * -
import base64
import time
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_daq as daq
import pandas as pd
import numpy as np
import numpy as np
import cv2
# prepare the data--begin
cases = pd.read_csv('../data/valid-acl.csv',
header=None,
names=['Case', 'Abnormal'],
dtype={
'Case': str,
'Abnormal': np.int64
}
)
case_list = cases['Case'].tolist()
predictions = pd.read_csv('./val_data.csv')
# prepare the data--end
app = dash.Dash()
# Boostrap CSS.
app.css.append_css({
'external_url': "https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css"
}) # noqa: E501
app.layout = html.Div(
[
html.Div(
html.H1(children='Interpretation of MRNet models through Class Activation Maps (CAM)',
style={
'text-align': 'center'
}
),
),
html.Div(
[
html.Div(
[
html.P('Select an MRI exam:'),
html.Div([
dcc.Dropdown(
id='cases',
options=[{
'label': case,
'value': case
}
for case in case_list
],
placeholder="Pick a case",
clearable=False
)
],
style={
'margin-bottom': 20
}
),
],
className='col-3',
style={
'margin-top': '10'
}
),
html.Div([
html.Div([
html.Div([
html.P('Select true labels :'),
dcc.RadioItems(
id="label_radioitems",
options=[
{'label': 'Positive (ACL tear)',
'value': 'acl'},
{'label': 'Negative (Normal)',
'value': 'normal'},
],
value='acl',
labelStyle={'display': 'inline-block'}
),
],
style={'float': 'left', 'width': '45%'}
),
html.Div([
html.P('Select predicted labels :'),
dcc.RadioItems(
id="pred_radioitems",
options=[
{'label': 'Positive (ACL tear)',
'value': 'acl'},
{'label': 'Negative (Normal)',
'value': 'normal'},
],
value='acl',
labelStyle={'display': 'inline-block'}
),
]
),
]),
],
className='col-6'
),
html.Div([
html.Div(id="number_of_cases"),
html.Span(
"txt",
id="label_badge",
className="badge badge-success badge-large",
style={'font-size': '15px'}
),
],
className='col-3'
)
],
className="row alert alert-success",
),
html.Div([
html.Div([
html.P(id='summary', style={'font-size': '20px'}),
html.Div([
html.Div('This probability is a weighted average of the three probabilities of tears over each plane', style={
'float': 'left', 'font-size': '20px'}),
html.Div('Slide over the slices of each MRI to inspect highlighted regions of tear as depicted by CAMs', style={
'float': 'left', 'font-size': '20px'}),
],
)
],
className="col-12 alert alert-info"),
],
className='row'
),
html.Hr(),
html.Div(
[
html.Div([
html.Div([
dcc.Slider(id='slider_axial', updatemode='drag')
],
style={'margin-right': '5px'}
),
html.Hr(),
html.P(id="score_axial", style={'text-align': 'center'}),
html.Div([
html.Div([
html.Img(
id="slice_axial",
),
],
style={'float': 'left', 'margin-right': '5px'}
),
html.Div([
html.Img(
id="cam_axial",
),
],
)
],
),
html.P(id="title_axial", style={'text-align': 'center'})
],
className="col-4"
),
html.Div([
html.Div([
dcc.Slider(id='slider_coronal', updatemode='drag')
],
style={'margin-right': '5px'}
),
html.Hr(),
html.P(id="score_coronal", style={'text-align': 'center'}),
html.Div([
html.Div([
html.Img(
id="slice_coronal",
),
],
style={'float': 'left', 'margin-right': '5px'}
),
html.Div([
html.Img(
id="cam_coronal",
),
],
)
],
),
html.P(id="title_coronal", style={'text-align': 'center'})
],
className="col-4"
),
html.Div([
html.Div([
dcc.Slider(id='slider_sagittal', updatemode='drag')
]),
html.Hr(),
html.P(id="score_sagittal", style={
'text-align': 'center'}),
html.Div([
html.Div([
html.Img(
id="slice_sagittal",
),
],
style={'float': 'left', 'margin-right': '5px'}
),
html.Div([
html.Img(
id="cam_sagittal",
),
],
)
],
),
html.P(id="title_sagittal", style={'text-align': 'center'})
],
className="col-4"
),
],
className='row'
)
],
className='container-fluid',
)
if __name__ == '__main__':
app.run_server(debug=True, port=8060)
Datasets
Models
