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Tags: Medical imaging

1.Slicer / Slicer

Multi-platform, free open source software for visualization and image computing.

medical-imaging
vtk
itk
python
qt
image-processing
+14 more

C++Other license2.7K total804 forks

+142 stars

from 2,564 · score 4.96

2.mahmoodlab / UNI

Pathology Foundation Model - Nature Medicine

foundation
foundation-model
histopathology
mahmoodlab
pathology
uni
+11 more

Jupyter Notebook Other license797 total96 forks

+38 stars

from 759 · score 3.66

3.mlmed / torchxrayvision

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

medical-imaging
medical-ai
deep-learning
machine-learning
transfer-learning
dataset
+13 more

Jupyter Notebook Other license1.2K total260 forks

+36 stars

from 1,180 · score 3.61

4.mahmoodlab / CONCH

Vision-Language Pathology Foundation Model - Nature Medicine

bioimage-analysis
bioimage-informatics
conch
foundation-model
health-informatics
histopathology
+9 more

Python Other license540 total54 forks

+21 stars

from 519 · score 3.09

5.fo-dicom / fo-dicom

Fellow Oak DICOM for .NET, .NET Core, Universal Windows, Android, iOS, Mono and Unity

dicom
netcore
medical
medical-imaging
dot-net
c-sharp
+4 more

C#Other license1.2K total671 forks

+15 stars

from 1,205 · score 2.77

6.sohmandal / classpose

A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources

bioimage-analysis
cell-classification
computer-vision
digital-pathology
foundation-models
microscopy

Python Other license39 total2 forks

+6 stars

from 33 · score 1.95

7.HALFpipe / HALFpipe

ENIGMA HALFpipe is a user-friendly software that facilitates reproducible analysis of fMRI data

neuroimaging
reproducibility
pipeline
meta-analysis
bids
fmri

Python Other license131 total23 forks

+3 stars

from 128 · score 1.39

8.CBICA / CaPTk

Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk

cancer-imaging-research
radiomics
radiomics-features
machine-learning
cpp
cpp11
+14 more

C++Other license198 total65 forks

+1 stars

from 197 · score 0.69

Order: Attention Score

Most net stars gained in the window first. The score only puts this gain on a log scale, so the order is the order of the gain.

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Ranked by stars gained in the window. The score is the natural log of one plus that gain, so a repository's rank is exactly its star gain's rank.

score = log1p(net stars gained in the trailing T complete UTC days)

A repository is ranked only when its Star History covers every day of the window and its latest Observation is not missing.

The longest window is 180 days. Star History from the Repository Host goes back at most 180 days before the first daily record that AwesomeRepos made for the repository. A longer window needs more of these daily records first. A repository younger than the window does not rank in it.

The filters select from the same eligible repositories. A repository matches the topics when it has any of them. Total stars is the count now. Last push and creation on the Repository Host count back from the start of today (UTC). The counts beside the languages, licenses, and topics include all ranked repositories in the window. A Tag holds the repositories that have it. See Tags.

A new repository enters the index when it gains stars faster than the published admission threshold, whatever its size. A repository stays in the index after it enters. Thus an archived repository can still rank.

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