misas 0.0.1

A library for segmentation model explainability through sensitivity analysis

Model Interpretation through Sensitivity Analysis for Segmentation

Interpret and explain your segmetation models through analysing their sensitivity to defined alterations of the input

Input alterations currently include:

• rotation
• cropping

Install

pip install misas

How to use

Example with kaggle data

from fastai.vision import *

#hide_output
img = lambda: open_image("example/kaggle/images/1-frame014-slice005.png")
trueMask = lambda: open_mask("example/kaggle/masks/1-frame014-slice005.png")
trainedModel = Fastai1_model('chfc-cmi/cmr-seg-tl', 'cmr_seg_base')
img().show(y=trueMask(), figsize=(8,8))

Rotation

plot_series(get_rotation_series(img(), trainedModel))

results = eval_rotation_series(img(), trueMask(), trainedModel)
plt.plot(results['deg'], results['c1'])
plt.plot(results['deg'], results['c2'])
plt.axis([0,360,0,1])

[0, 360, 0, 1]

You can use interactive elements to manually explore the impact of rotation

from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets

rotation_series = get_rotation_series(img(),trainedModel,step=10)

def plot_rotation_frame(deg):
    return plot_frame(*rotation_series[int(deg/10)], figsize=(10,10))

#hide_output
interact(
    plot_rotation_frame,
    deg=widgets.IntSlider(min=0, max=360, step=10, value=90, continuous_update=False)
)

There are lots of other transformations to try (e.g. cropping, brightness, contrast, ...). For a complete list see the local_interpret documentation.