| """ | |
| Data: https://drive.google.com/drive/folders/1zPK--rbdnqK2T0NnTrfvujeSg4PkN_bH?usp=sharing (contains normal_001.tif) | |
| Alternatively: https://aws.amazon.com/marketplace/pp/prodview-exkvqrznup6vc?sr=0-1&ref_=beagle&applicationId=AWSMPContessa#resources | |
| """ | |
| import os | |
| import sys | |
| import timeit | |
| import numpy as np | |
| sys.path.append('C:\\Program Files\\ASAP 2.1\\bin') # https://github.com/computationalpathologygroup/ASAP/releases/tag/ASAP-2.1 |
cmake command for ASAP.
The order in which I build the dependencies is in the same order as defined using the find_package() function in ASAP/CMakeLists.txt.gcc --version is >=9 since otherwise it leads to issues with compiling #include in some .cpp files like [core/filetools.cpp](https://github.com/computationalpatholo
| """ | |
| CAMELYON 16 DATASET | |
| - Whole Slide Images (WSI) containing histopathological information on breast cancer | |
| 1. Download | |
| - To view the list of AWS | |
| - Link: https://aws.amazon.com/marketplace/pp/prodview-exkvqrznup6vc?sr=0-1&ref_=beagle&applicationId=AWSMPContessa#resources | |
| - Click on Resources on AWS --> View Resources | |
| - Single Sample | |
| - aws s3 cp --no-sign-request s3://camelyon-dataset/CAMELYON16/images/tumor_032.tif ./raw/tumor_032.tif |
Samples are writen down with their StudyInstanceUID as the header.
StudyInstanceUID, you can use Postman client and the TCIA-NBIA api to extract seriesInstanceUID and sopInstanceUID
| """ | |
| Motivation: Understanding Convolution for Semantic Segmentation (https://arxiv.org/pdf/1702.08502.pdf) | |
| : https://stats.stackexchange.com/questions/265462/whats-the-receptive-field-of-a-stack-of-dilated-convolutions | |
| """ | |
| import pdb | |
| import traceback | |
| import numpy as np | |
| import matplotlib.pyplot as plt |
| import gc | |
| import sys | |
| import humanize # pip install humanize | |
| def actualsize(input_obj): | |
| # https://towardsdatascience.com/the-strange-size-of-python-objects-in-memory-ce87bdfbb97f | |
| memory_size = 0 | |
| ids = set() | |
| objects = [input_obj] | |
| while objects: |
| #!/bin/bash | |
| #SBATCH --job-name=<script-name> | |
| #SBATCH --partition=<partition-name> # see qq command above | |
| #SBATCH --gres=gpu:1 | |
| #SBATCH --nodelist=<node-name> # see qq command above | |
| #SBATCH --ntasks=1 | |
| #SBATCH --cpus-per-task=6 | |
| #SBATCH --mem-per-cpu=8G | |
| #SBATCH --time=14-20:00:00 | |
| #SBATCH --mail-type=end |
| """ | |
| This script shall plot contours using cv2 on an image | |
| - Useful for plotting organ contours on a medical image | |
| -- Note: Organ Mask should contain labels values in the [0,255] range | |
| """ | |
| # Standard Libraries | |
| import pdb | |
| import cv2 | |
| import numpy as np |
| """ | |
| References | |
| 1. Calculating area under the curve (AUC) using Riemann Sum - https://en.wikipedia.org/wiki/Riemann_sum | |
| 2. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc | |
| - it uses np.trapz() | |
| - Code: https://github.com/numpy/numpy/blob/v1.22.0/numpy/lib/function_base.py#L4686-L4797 | |
| 3. Other methods implemented in TFlow | |
| - https://wikidocs.net/24605 | |
| - Note: This method only works right when we provide a list x that has evenly spaced values |
| """ | |
| OG Ref: https://github.com/tensorflow/probability/issues/620 | |
| Goals | |
| - To experiment with DNNs built using Flipout layers in both eager and non-eager mode | |
| - Eager mode allows for debugging using tf.print() | |
| - Non-eager mode is supposed to be faster and less memory consuming since its pre-computes functions in a graph | |
| - Models will be made using functional API and the dataset will not use tf.data.Dataset | |
| Notes |