misas 0.1.1
Model Interpretation through Sensitivity Analysis for Segmentation
License: MIT License
Requires Python: >=3.7
Keywords: interpretability,explainability,machine learning,sensitivity,augmentation

Model Interpretation through Sensitivity Analysis for Segmentation
================

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

![](./logo.svg)

Input alterations currently include: - rotation - cropping -
brightness - contrast - zooming - flipping (dihedral) - resizing - MR
artifacts (via [`torchio`](https://github.com/fepegar/torchio))

## Install

`pip install misas`

## Cite

If you use `misas` in your research, please cite: \> Ankenbrand, M. J.,
Shainberg, L., Hock, M., Lohr, D., & Schreiber, L. M. Sensitivity
analysis for interpretation of machine learning based segmentation
models in cardiac MRI. BMC Medical Imaging, 21(27).
https://doi.org/10.1186/s12880-021-00551-1

If you use the simulated MR artifacts, please also cite `torchio`: \> F.
Pérez-García, R. Sparks, and S. Ourselin. TorchIO: a Python library for
efficient loading, preprocessing, augmentation and patch-based sampling
of medical images in deep learning. Computer Methods and Programs in
Biomedicine (June 2021), p. 106236. ISSN: 0169-2607.
https://doi.org/10.1016/j.cmpb.2021.106236

## How to use

Example with kaggle data

``` python
from misas.core import *
from misas.core import default_cmap, default_cmap_true_mask
from misas.fastai_model import Fastai2_model
from PIL import Image, ImageEnhance, ImageOps
from functools import partial
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import numpy as np
```

``` python
def label_func(x):
    pass
def acc_seg(input, target):
    pass
def diceComb(input, targs):
    pass
def diceLV(input, targs):
    pass
def diceMY(input, targs):
    pass
```

``` python
img = lambda: Image.open("example/kaggle/images/1-frame014-slice005.png").convert("RGB")
trueMask = lambda: Image.open("example/kaggle/masks/1-frame014-slice005.png").convert("I")
trainedModel = Fastai2_model("chfc-cmi/cmr-seg-tl", "cmr_seg_base", force_reload=False)
fig, ax = plt.subplots(figsize=(8,8))
ax.imshow(np.array(img()))
ax.imshow(np.array(trueMask()), cmap=default_cmap_true_mask, alpha=.5, interpolation="nearest")
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
```

    Using cache found in /home/csa84mikl/.cache/torch/hub/chfc-cmi_cmr-seg-tl_master

![](index_files/figure-gfm/cell-4-output-2.png)

### Rotation

``` python
plot_series(get_rotation_series(img(), trainedModel))
```

      0%|          | 0/7 [00:00<?, ?it/s]

    [W NNPACK.cpp:51] Could not initialize NNPACK! Reason: Unsupported hardware.

![](index_files/figure-gfm/cell-5-output-3.png)

``` python
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%|          | 0/72 [00:00<?, ?it/s]

    (0.0, 360.0, 0.0, 1.0)

![](index_files/figure-gfm/cell-6-output-3.png)

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

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

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

      0%|          | 0/37 [00:00<?, ?it/s]

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

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

    interactive(children=(IntSlider(value=90, continuous_update=False, description='deg', max=360, step=10), Outpu…

    <function __main__.plot_rotation_frame(deg)>

There are lots of other transformations to try (e.g. cropping,
brightness, contrast, …) as well as MR specific artifacts.

## Overview

This is the schematic overview of how `misas` works. Created with the
amazing [Excalidraw](https://excalidraw.com/). ![schema](schema.svg)

## Logo

The logo was designed by Markus J. Ankenbrand using: - [Open box / Boite
ouverte](https://openclipart.org/detail/29155/open-box-boite-ouverte) by
[SimpleIcons](https://openclipart.org/artist/SimpleIcons) via
[openclipart.org](https://openclipart.org) - [Cutter
icon](https://openclipart.org/detail/213227/cutter-icon) by
[SimpleIcons](https://openclipart.org/artist/SimpleIcons) via
[openclipart.org](https://openclipart.org), original by Marco Olgio, via
[WikiMedia](commons.wikimedia.org/wiki/File:Utility_Knife_by_Marco_Olgio.svg) -
[Hack Font](https://sourcefoundry.org/hack/) -
[Inkscape](https://inkscape.org)

![](./logo.svg)

## Attribution

This project is inspired by the awesome [“Is it a Duck or Rabbit”
tweet](https://twitter.com/minimaxir/status/1103676561809539072) by
[@minimaxir](https://twitter.com/minimaxir). Also check out the
[corresponding
repo](https://github.com/minimaxir/optillusion-animation).

``` html
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Is it a Duck or a Rabbit? For Google Cloud Vision, it depends how the image is rotated. <a href="https://t.co/a30VzjEXVv">pic.twitter.com/a30VzjEXVv</a></p>&mdash; Max Woolf (@minimaxir) <a href="https://twitter.com/minimaxir/status/1103676561809539072?ref_src=twsrc%5Etfw">March 7, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
```

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Is it a Duck or a Rabbit? For Google Cloud Vision, it depends how the image is rotated. <a href="https://t.co/a30VzjEXVv">pic.twitter.com/a30VzjEXVv</a></p>&mdash; Max Woolf (@minimaxir) <a href="https://twitter.com/minimaxir/status/1103676561809539072?ref_src=twsrc%5Etfw">March 7, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> 

## Changes

### 0.1.0 \<2022-07-14\>

-   Re-write internal function to use pillow instead of fastai (version
    1)

### 0.0.4 \<2021-01-14\>

-   Initial release