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ISIC Archive API

Project description

isic-archive (ISIC Archive access python module)

The ISIC Archive is an online repository and archive published and maintained by the International Skin Imaging Collaboration. Next to the human-readable and browsable website, it also provides a publicly available API, which offers several functions (called endpoints) for interacting with the data programmatically.

The present python package is an attempt at bundling some of the more frequently used functionality into a set of modules, thus reducing the need to re-write certain code for a diverse set of projects.

First steps

To start interacting with the archive through its API, import the IsicApi class from the isicarchive.api module, and then create an instance of the class:

from isicarchive.api import IsicApi
api = IsicApi()

The return object variable, api, then allows you to query the web-based API through method calls, which will typically create further object variables (such as study or image objects).

Data availability

All general features are available without logging into the API. However, since many images (as well as studies using those images) have not been marked as being "publicly available", the number of items returned by many functions (endpoints) differs based on whether you have (successfully) authenticated with the API. If you do not plan to register a username, you can skip the next section, and either set the username parameter to None or skip it altogether in the constructor call to IsicApi.

Logging into the ISIC Archive

You can provide your username as the first parameter when creating the IsicApi object, as well as an optional password parameter:

# set username
username = ''

# create API object
api = IsicApi(username)

# or, if you can securely store a password as well
api = IsicApi(username, password)

Please do not enter the password in clear text into your source code. If you provide only the username, the password will be requested from either the console or, if used in a Jupyter notebook, below the active cell using the getpass library. You can also store the password in the .netrc file (see the GNU page on the .netrc file format) in your user's home folder.

Local cache folder

Since a lot of the data that can be retrieved from the archive (API) is static--that is, for instance images won't change between uses of the API--you can keep a locally cached copy, which will speed up processing of data on the next call you use the same image or annotation, for instance. To do so, please add the cache_folder parameter to the call, like so:

# For Linux/Mac
cache_folder = '/some/local/folder'
# For Windows
cache_folder = 'C:\\Users\\username\\some\\local\\folder'

# Create object (without username)
api = IsicApi(cache_folder=cache_folder)
# (or with username)
api = IsicApi(username, cache_folder=cache_folder)
# (or with username and password)
api = IsicApi(username, password, cache_folder=cache_folder)

Relatively large and complex data (annotations, images, etc.) will then have a stored local copy, which means that they can be retrieved later from the cache, instead of having to request them again via the web-based API.

Within the cache folder the IsicApi object will, on first use, create a two-level hierarchy of 16 subfolders each, named 0 through 9, and a through f (the 16 hexadecimal digits), to avoid downloading too many files into a single folder, which would slow down the operation later on. For each file, the sub-folder is determined by the last hexadecimal digits of the unique object ID (explained below).

Images are stored with a filename pattern of image_[objectId]_[name].ext whereas objectId is the unique ID for this image within the archive, name is the filename (typically ISIC_xxxxxxx), and .ext is the extension as provided by the Content-Type header of the downloaded image.

Superpixel images (also explained below) are stored with the filename pattern of spimg_[objectId].png using the associated image's object ID!

Caching information about all images

Since the archive contains several thousand images, it can often be helpful to be able to search for specific images. To do so locally, you can download the details about all images available in the archive (works only if you've created the IsicApi object with the cache_folder parameter) like so:

# Populate image cache

# display information about image ISIC_000000 (by its ID) from the cache
image_info = api.image_cache[api.images['ISIC_0000000']]

When called for the first time, building the cache may take several minutes. Once the information is downloaded, however, only a single call will be made to the web-based API to confirm that, indeed, no new images are available. For this to work, however, it is important that you do not use the same cache folder for sessions where you are either logged in (authenticated) versus not! The cache file itself will be stored in the file named [cache_folder]/0/0/imcache_000000000000000000000000.json.gz.

Finally, feature annotations associated with a specific study can be downloaded in bulk and cached using this syntax:

# Load annotation markup feature superpixel arrays

The resulting file will be stored in stann_[objectId].json.gz, and not for each annotation object separately, so that loading will be much faster.

Debugging of API calls

Since it is sometimes helpful to understand which calls to the web-based API are made, you can provide a debug parameter (set to True) to the IsicApi(...) call:

api = IsicApi(debug=True)
# or, for instance
api = IsicApi(username, password, cache_folder=cache_folder, debug=True)

If debug is set to true (which can also be enabled later in the session, by setting api._debug = True), every HTTP GET request made to the ISIC Archive will be printed out to the console, like this:

Requesting (auth)
Requesting (auth) with params: {'limit': 0, 'detail': 'true'}
Requesting (auth) with params: {'limit': 0, 'detail': 'true'}

Some more details on the web-based API

Any interaction with the web-based API is performed by the IsicApi object through the HTTPS protocol, using the appropriate requests package methods. As part of the requests made, the endpoint (function and type of element being interacted with) is specified, and one or several parameters can be set, which are appended to the URL. For instance, retrieving information about one specific image would be achieved by accessing the following URL:

Object IDs and element representation

This last portion of the URL that appears after the image/ part is called the (object) id, and is a system-wide unique value that identifies each element to ensure that one interacts only with the intended target. The output of the URL above is (slightly truncated for brevity):

  "_id": "5436e3abbae478396759f0cf",
  "_modelType": "image",
  "created": "2014-10-09T19:36:11.989000+00:00",
  "creator": {
    "_id": "5450e996bae47865794e4d0d",
    "name": "User 6VSN"
  "dataset": {
    "_accessLevel": 0,
    "_id": "5a2ecc5e1165975c945942a2",
    "description": "Moles and melanomas.",
    "license": "CC-0",
    "name": "UDA-1",
    "updated": "2014-11-10T02:39:56.492000+00:00"
  "meta": {
    "acquisition": {
      "image_type": "dermoscopic",
      "pixelsX": 1022,
      "pixelsY": 767
    "clinical": {
      "age_approx": 55,
      "anatom_site_general": "anterior torso",
      "benign_malignant": "benign",
      "diagnosis": "nevus",
      "diagnosis_confirm_type": null,
      "melanocytic": true,
      "sex": "female"
  "name": "ISIC_0000000",
  "updated": "2015-02-23T02:48:17.495000+00:00"

Pretty much all elements available through the API are returned in the form of their JSON representation (notation) as text. Lists of elements are returned as arrays. The exception are binary blobs (such as image data, superpixel image data, and mask images).

Within the ISIC archive (and thus for the API), the following elements are recognized:

  • datasets (a series of images that were uploaded, typically at the same time, as a somewhat fixed set)
  • studies (selection of images, possibly from multiple datasets, together with questions and features to be annotated by users)
  • images (having both a JSON and several associated binary blob elements)
  • segmentations (also having a JSON and a binary mask image component)
  • annotations (responses to questions and image-based per-feature annotation as a selection of "superpixels")
  • users (information about each registered user)
  • tasks (information about tasks assigned to the logged in user)

Of these, currently accessible via the IsicApi object are dataset, study, image, segmentation, and annotation, whereas users and tasks are not meaningfully implemented as separate objects at this time.

Image superpixels

As part of the image processing capabilities of the ISIC Archive itself, each image that is uploaded will be automatically compartmentalized into about 1,000 patches of roughly equal size. E.g. for an image with a 4-by-3 aspect ratio, there would be roughly 36 times 27 superpixels. The superpixel information is stored in a specifically RGB-encoded image, such that for each superpixel the patch has a (for the computer uniquely represented) RGB color code:

ISIC_0000000 image superpixels

The IsicApi.image.Image class contains functions to decode and map this image first into an index array, and then into a mapping array:

from isicarchive.api import IsicApi

# load superpixel image for first image
api = IsicApi()
image = api.image('ISIC_0000000')
superpixel_index_image = image.superpixels['idx']
superpixel_mapping = image.superpixels['map']

This mapping array can be used to rapidly access (e.g. extract or paint over) the pixels in the actual color image of a skin lesion:

# paint over superpixel with index 472 in an image with red (RGB=(255,0,0))
image = api.image('ISIC_0000000')
image_data =
image_shape = image_data.shape
image_data.shape = (image_shape[0] * image_shape[1], -1)
map = image.superpixels['map']
superpixel_index = 472
pixel_count = map[superpixel_index, -1]
superpixel_pixels = map[superpixel_index, 0:pixel_count]
image_data[superpixel_pixels, 0] = 255
image_data[superpixel_pixels, 1] = 0
image_data[superpixel_pixels, 2] = 0
image_data.shape = image_shape

# show image
import matplotlib.pyplot as plt
%matplotlib inline

Retrieving information about a study

The syntax below will make a call to the web-based API, and retrieve the information about the study named in the first parameter. If the study is not found, an exception is raised! Other than the web-based API (which does not support study names), you do not have to look up the object ID manually first. The returned value, study, is an object of type, which provides some additional methods.

# Retrieve study object
study ='ISBI 2016: 100 Lesion Classification')

# Download all accessible images and superpixel images for this study

In addition to the information regularly provided by the ISIC Archive API, the IsicApi object's implementation can also used to mass-download information about all annotations.

# Print study features

Retrieving information about a dataset

dataset = api.dataset(dataset_name)

Similarly to a study, this will create an object of type isicarchive.dataset.Dataset, which allows additional methods to be called.

In addition to the information regularly provided by the ISIC Archive API, the IsicApi object's implementation will also attempt to already download information about the access list, metadata, and images up for review.

Retrieving images

# Load the first image of a loaded study
image = api.image(study.images[0])

This will, initially, only load the information about the image. If you would like to make the binary data available, please use the following methods:

# Load image data

# Load superpixel image data

# Parse superpixels into a mapping-to-pixel-indices array

# Load the associated (highest-quality) segmentation
segmentation = image.load_segmentation()

The mapping of an image's superpixel RGB image takes a few hundred milliseconds, but storing the map in a different format would be relatively wasteful, and so this seems preferable.

Selecting images

Once all image information has been cached, the IsicApi object allows to select images based on the contents of any subfield in the image details (JSON) representation:

# Make initial selection
selection = api.select_images([
    ['meta.acquisition.pixelsX', '>=', 2048],
    ['meta.acquisition.image_type', '==', 'dermoscopic'],
    ['meta.clinical.diagnosis', '!=', 'nevus'],

# refine selection (you can inspect the results after each step)
selection = api.select_images(['dataset._accessLevel', '==', 0], sub_select=True)
selection = api.select_images(['notes.tags', 'ni', 'ISBI 2017: Training'], sub_select=True)
selection = api.select_images(['meta.unstructured.biopsy done', '==', 'Y'], sub_select=True)
selection = api.select_images(['meta.clinical.melanocytic', 'is', True], sub_select=True)

The selection will both be returned, and also stored in the api.image_selection field. So, in a Jupyter notebook, please assign the result to a variable if it is the last statement in a cell and you wish not to print the output!

Memory requirements

At this time, by default all objects that are being created are also stored in the api object's internal attributes, such that an image or study that has been made into an object no longer requires a second API call later on. This also means that data that is loaded into an object (especially image data into an image, segmentation, or annotation object) will remain in memory, unless it is expressly removed (cleared). This can be done by calling the object.clear_data() method. Depending on the object type, additional flags can be provided. The default is to clear all binary (large) data, but keep object references (e.g. between images and datasets, etc.) intact.

Most data will be cleared by calling this method without any further parameters on the api object itself:



This section contains information about the package.

Author information

isicarchive is being developed by Jochen Weber, who works at Memorial Sloan Kettering Cancer Center in New York City. He is supported by Nick Kurtansky and Dr. Konstantinos Liopyris (both MSKCC as well) and collaborates closely with Brian Helba and Dan LaManna (both with Kitware), who work on the web-based API. Additional support and code is being provided by Prof. David Gutman, MD, PhD.


  • 10/3/2019
    • fixed Jupyter notebook progress bar widget
  • 9/30/2019
    • added image cropping function
  • 9/26/2019
    • added code to extract information from border pixels
  • 9/23/2019
    • added reedsolo module for encoding data into border pixels
  • 9/16/2019
    • created method to generate heatmaps across all study images (homogeneous options)
  • 9/12/2019
    • study.image_heatmaps(...) now adds legends and exemplar feature to montage
    • all features now carry a valid synonyms list (with self as sole entry, if necessary)
  • 9/11/2019
    • changed cache to two-level strategy, which allows all ISIC images to be stored
  • 9/10/2019
    • preparation for extended heatmaps (montage of original and heatmaps)
  • 9/9/2019
    • more work on CSV support, extracting data from read CSV files
  • 9/6/2019
    • initial support for CSV input and output
  • 9/5/2019
    • added meta data collection and extraction methods for image and study
    • rewrote Sampler class to be more succinct (and JIT compatible)
  • 9/4/2019
    • added (Font class) and code to add correctly set text to images (in IsicApi)
  • 9/3/2019
    • added study.image_heatmap to color annotations on (photographic) image
  • 9/2/2019
    • implemented Dice and pixel-wise cross-correlation (for annotation overlap)
  • 8/30/2019
    • added more functions for image coloring
  • 8/29/2019
    • added (list of known features) for later selection (and colors)
    • added status codes and other features to __main__ (python -m ...)
    • refactored all .get calls to api.get (other than authentication)
    • refactored all image-related function into module (faster import of func)
    • refactored major external package imports (imageio, numba, numpy) to be processed late
  • 8/28/2019
    • added .netrc support (storing a password for python -m ... mode)
    • added minimal command line ( for python -m ...) syntax
    • preparing for version 0.4.8 to be released
    • implemented the clear_data(...) methods for all objects
    • added David's superpixel contour JSON output format
    • implemented a "superpixel in segmentation mask" method
    • fixed a bug that would not use the auth_token when accessing segmentations
  • 8/27/2019
    • first working version of superpixel outline SVG paths
  • 8/26/2019
    • removed func import in
    • moved two functions from to (smaller modules)
    • worked on converting superpixel map to SVG paths
  • 8/22/2019
    • began work on segmentations-related code
    • updated image.Image.show_in_notebook to use func.display_image
    • added some more documentation
    • improved func.getxattr by adding -index and name=val syntax
    • moved cache_filename from func module to api.IsicApi class
  • 8/21/2019
    • added infrastructure for conda-forge (thanks to Marius van Niekerk)
    • began refactoring test code (with unit testing)
    • added api.select_images(...) to select images from the entire archive
    • added func.selected(...) and func.select_from(...) for selection logic
    • improved func module with better Typing hints (and general cleanup)
    • added func.write_image(...) to write out images, including into a buffer
    • moved code from Annotation.show_in_notebook(...) to func.color_superpixels(...)
  • 8/20/2019
    • added func.print_progress(...) function (text-based progress bar)

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