Skip to main content

Histopathological image analysis using Grad-CAM representation map

Project description


Powered by Paper

PyPI - Python Version PyPI PyPI - Downloads PyPI - License

HipoMap_Logic HipoMap_howtomakeHM

HipoMap is slide-based histopathology analysis framework in which a disease-specific graphical representation map is generated from each slide. Further, HipoMap, which is a small and fixed size, is introduced as input for machine-learning models instead of extremely large and variable size WSI. HipoMap is obtained using gradients of patch probability scores to represent disease-specific morphological patterns. Proposed HipoMap based whole slide analysis has outperformed current state-of-art whole slide analysis methods. We assessed the proposed method on Lung Cancer WSI images and interpreted the model based on class probability scores and HipoMap scores. A pathologist clinically verified the results of interpretation.

It provides:

  • a powerful ...
  • ...



OpenSlide is a C library that provides a simple interface to read whole-slide images (also known as virtual slides). The current version is 3.4.1, released 2015-04-20.

For Linux (Fedora), you can install latest version of OpenSlide by running following commands from terminal:

$ dnf install openslide

For Linux (Debian, Ubuntu), you can install latest version of OpenSlide by running following commands from terminal:

$ apt-get install openslide-tools

For Linux (RHEL, CentOS), you can install latest version of OpenSlide by running following commands from terminal:

$ yum install epel-release
$ yum install openslide

For MacOSX, you can install latest version of OpenSlide by running following commands from terminal:

$ brew install openslide

For Window, you can install latest version of OpenSlide:


Latest PyPI stable release

PyPI PyPI - Downloads

pip install HipoMap


PyPI - Python Version PyPI PyPI PyPI PyPI PyPI PyPI PyPI PyPI PyPI


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.core import generate_hipomap

generate_hipomap(inputpath="<path>/Dataset/", outputpath="<path>/Rep/", model=model,
                layer_name="block5_conv3", patch_size=(224, 224))

Drawing heatmap with representation map

# Draw heatmap
from hipomap.core import draw_represent

draw_represent(path="<path>/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 representation map generated by train / validation / test set.

# Classify data to cancer/normal with representation map
from hipomap.core import HipoClass

hipo = HipoClass(K=50)

# 1. Split data with base(.csv) 
trainset, validset, testset = hipo.split("split.csv", dir_normal="<path>/Dataset/Normal/",

# 2. Train the classifier
hipo_model =, validset, lr=0.1, epoch=20, batch_size=1)

# 3. Get prediction value
prediction = hipo.predict_with_test(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')

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hipomap-1.0.2.tar.gz (24.6 kB view hashes)

Uploaded source

Built Distribution

hipomap-1.0.2-py3-none-any.whl (56.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page