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Automated BioMedical Information Curation for Machine Learning Applications.

Project Description

BioVida is a library designed to make it easy to gain access to existing data sets of biomedical images as well as build brand new, custom-made ones.

It is hoped that by automating the tedious data munging that is typically involved in this process, more people will become interested in applying machine learning to biomedical images and, in turn, advancing insights into human disease.

In a nod to recursion, BioVida tries to accomplish some of this automation with machine learning itself, using tools like convolutional neural networks.


Python Package Index:

$ pip install biovida

Latest Build:

$ pip install git+git://

Requires Python 3.4+

Images: Stable

In just a few lines of code, you can gain access to biomedical databases which store tens of millions of images.

Please note that you are bound to adhere to the copyright and other usage restrictions under which this data is provided to you by its creators.

Open-i BioMedical Image Search Engine

# 1. Import the Interface for the NIH's Open-i API.
from biovida.images import OpeniInterface

# 2. Create an Instance of the Tool
opi = OpeniInterface()

# 3. Perform a search for x-rays and cts of lung cancer'lung cancer', image_type=['x_ray', 'ct'])  # Results Found: 9,220.

# 4. Pull the data
search_df = opi.pull()

Cancer Imaging Archive

# 1. Import the interface for the Cancer Imaging Archive
from biovida.images import CancerImageInterface

# 2. Create an Instance of the Tool
cii = CancerImageInterface(YOUR_API_KEY_HERE)

# 3. Perform a search'esophageal')

# 4. Pull the data
cdf = cii.pull()

Both CancerImageInterface and OpeniInterface cache images for later use. When data is ‘pulled’, a records_db is generated, which is a dataframe of all text data associated with the images. They are provided as class attributes, e.g., cii.records_db. While records_db only stores data from the most recent data pull, cache_records_db dataframes provides an account of all image data currently cached.

Splitting Images

BioVida can divide cached images into train/validation/test.

from biovida.images import image_divvy

# 1. Define a rule to 'divvy' up images in the cache.
def my_divvy_rule(row):
    if row['image_modality_major'] == 'x_ray':
        return 'x_ray'
    elif row['image_modality_major'] == 'ct':
        return 'ct'

# 2. Define Proportions and Divide Data
tt = image_divvy(opi, my_divvy_rule, action='ndarray', train_val_test_dict={'train': 0.8, 'test': 0.2})

# 3. The resultant ndarrays can be unpacked as follows:
train_ct, train_xray = tt['train']['ct'], tt['train']['x_ray']
test_ct, test_xray = tt['test']['ct'], tt['test']['x_ray']

Images: Experimental

Automated Image Data Cleaning

Unfortunately, the data pulled from Open-i above is likely to contain a large number of images unrelated to the search query and/or are unsuitable for machine learning.

The experimental OpeniImageProcessing class can be used to completely automate this data cleaning process, which is partly powered by a Convolutional Neural Network.

# 1. Import Image Processing Tools
from biovida.images import OpeniImageProcessing

# 2. Instantiate the Tool using the OpeniInterface Instance
ip = OpeniImageProcessing(opi)

# 3. Analyze the Images
idf =

# 4. Use the Analysis to Clean Images

It is easy to split these images into training and test sets.

from biovida.images import image_divvy

def my_divvy_rule(row):
    if row['image_modality_major'] == 'x_ray':
        return 'x_ray'
    elif row['image_modality_major'] == 'ct':
        return 'ct'

tt = image_divvy(ip, my_divvy_rule, action='ndarray', train_val_test_dict={'train': 0.8, 'test': 0.2})
# These ndarrays can be unpack as shown above.

Genomic Data

While primarily focused on images, BioVida also provides a simple interface for obtaining related information, such genomic data.

# 1. Import the Interface for
from biovida.genomics import DisgenetInterface

# 2. Create an Instance of the Tool
dna = DisgenetInterface()

# 3. Pull a Database
gdf = dna.pull('curated')

Diagnostic Data

BioVida also makes it easy to obtain diagnostics data.

Information on disease definitions, families and synonyms:

# 1. Import the Interface for
from biovida.diagnostics import DiseaseOntInterface

# 2. Create an Instance of the Tool
doi = DiseaseOntInterface()

# 3. Pull the Database
ddf = doi.pull()

Information on symptoms associated with diseases:

# 1. Import the Interface for Disease-Symptoms Information
from biovida.diagnostics import DiseaseSymptomsInterface

# 2. Create an Instance of the Tool
dsi = DiseaseSymptomsInterface()

# 3. Pull the Database
dsdf = dsi.pull()

Unifying Information

The unify_against_images function integrates image data information against DisgenetInterface, DiseaseOntInterface and DiseaseSymptomsInterface.

from biovida.unification import unify_against_images

unify_against_images(interfaces=[cii, opi], db_to_extract='cache_records_db')

Left side of DataFrame: Image Data Alone

  article_type image_id image_ca ption modality_best_guess age sex disease
0 case_re port 1 Magnetic Resonance Imaging (MRI) 73 male fibroma
1 case_re port 2 Magnetic Resonance Imaging (MRI) 73 male fibroma
2 case_re port 1 Computed Tomography (CT): angiography 45 femal e bile duct cancer

Right side of DataFrame: Added Information

disease_famil y disease_sy nonym disease_d efinition known_associ ated_symptom s mentioned_symptoms known_assoc iated_genes
(cell type benign neoplasm,) nan nan (abdominal pain,…) (pain,) ((ANTXR2, 0.12), …)
(cell type benign neoplasm,) nan nan (abdominal pain,…) (pain,) ((ANTXR2, 0.12), …)
(biliary tract cancer,) (bile duct tumor,…) A biliary tract… (abdominal obesity,..) (colic,) nan


For more information on how to contribute, see the contributing document.

Bug reports and feature requests are always welcome and can be provided through the Issues page.


The resources document provides an account of all data sources and scholarly work used by BioVida.

Release History

This version
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