hipomap 0.1.3

Histopathological image analysis using Grad-CAM representation map

HipoMap

Installation

HipoMap support Python 3. It requires numpy, pandas, tensorflow, scipy, scikit-learn, seaborn, matplotlib, openslide-python, cv2.

Quick installation of openslide

• Update system

sudo apt-get update

• install openslide-tools

sudo apt-get install openslide-tools

• install openslide

pip install openslide

• install HipoMap

pip install HipoMap

Documentation

Quick Start

#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
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))

#draw heatmap
from HipoMap.hipoMap import draw_represent
draw_represent(path="/home/yeon/Dataset/", K=50, max_value=1000, save=False)

#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])