Metadata-Version: 2.1
Name: HipoMap
Version: 0.1.1
Summary: Histopathological image analysis using Grad-CAM representation map
Home-page: https://github.com/datax-lab/HipoMap
Author: Jeongyeon Park
Author-email: ParkJYeon2808@gmail.com
License: UNKNOWN
Description: # HipoMap
        
        ![](Capture.PNG)
        
        ## 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
        ```python
        #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])
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
