hipomap 0.2.2

Histopathological image analysis using Grad-CAM representation map

HipoMap

Features

Installation

HipoMap support Python 3.6+.

It can be installed through pip, because it is registered and provided with PyPI.

Dependencies

• Python3 (v3.6 or later)
• Numpy
• Pandas
• Tensorflow (v 1.15 or 2.x)
• Openslide-python
• Scipy
• Scikit-learn
• Seaborn
• Matplotlib
• Cv2

Installation

Most of dependencies will be installed automatically during installing HipoMap. However, for openslide-python, you can install it more easily by doing the following:

• Install openslide-tools

$ sudo apt-get update
$ sudo apt-get install openslide-tools

• Install openslide-python

$ pip install openslide

After installing openslide, you can install HipoMap with pip3 (with pip in a venv environment). Q

• Install HipoMap

$ pip3 install HipoMap

Documentation

Quick Start

Generating Whole-Slide Image based representation map

#Model load

#If you want to loaded keras pre-trained model
from tensorflow.keras.applications.vgg16 import VGG16

model = VGG16()

#If you want to loaded your pre-trained model(.h5 file)
from tensorflow.keras.models import load_model 

model = load_model(r'./pre_model.h5')

#Make representation map
from HipoMap.hipoMap import generateHipoMap

generateHipoMap(inputpath="/home/user/Dataset/", outputpath="/home/user/Rep/", model = model, layer_name="block5_conv3", patch_size=(224, 224))

Drawing heatmap with representation map

#Draw heatmap
from HipoMap.hipoMap import draw_represent

draw_represent(path="/home/yeon/Dataset/", K=50, max_value=1000, save=False)

Classify to Cancer/Normal with representation map

In this step, you must have a baseline file(.csv) for dividing each representatino map generated by train / validation / test set.

#Classify data to cancer/normal with representation map
from HipoMap.hipoClassify import HipoClass
hipo = HipoClass(K=50)

#1. Split data with base(.csv) 
trainset, validset, testset = hipo.split("./split.csv", dir_normal="/home/user/Dataset/Normal/", dir_cancer="/home/user/Dataset/Cancer")

#2. Train the classifier
hipo_model = hipo.fit(trainset, validset, lr=0.1, epoch=20, batchsize=1, activation_size=196)

#3. Get prediction value
prediction = hipo.predict(test_X=testset[0])

#4. Get score (tpr, fpr, auc)
tpr, mean_fpr, auc = hipo.evaluate_score(label=testset[1], prediction=prediction)

Generate Probmap with probability score

#Creating probability score array 
from HipoMap.scoring import scoring_probmap
scoring_probmap(path_model = "./pre_model.h5",
                path_data = "./Dataset/Test/",
                path_save = "./Result/prob_test/" 
               )

#Generating Probmap
from HipoMap.probmap import generating_probmap
generating_probmap(path_data='./Dataset/Test/',
                   path_prob='./Result/prob_test/',
                   path_save='./Result/probmap'
                  )