rahmaniitp Goto Github PK

python-cheatsheet

All-inclusive Python cheatsheet

python-exercise

python_for_image_processing_apeer

https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

quickannotator

An open-source digital pathology based rapid image annotation tool

radiomics

MATLAB programming tools for radiomics analysis

radiomics-develop

A repository to develop code for IBSI compliant radiomics applications

radiomics-features-extractor

Hand-crafted radiomics and deep learning-based radiomcis features extraction.

resource-dl-python-best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

scientific-visualization-book

An open access book on scientific visualization using python and matplotlib

segmentation-eval

Extract and evaluate radiomics for liver cancer tumors from DICOM segmentation masks. Using SimpleITK, PyRadiomics and PyDicom.

sifa

[TMI'20, AAAI'19] Synergistic Image and Feature Adaptation

simpleitk-notebooks

Jupyter notebooks for learning how to use SimpleITK

statistical-analysis-python-tutorial

Statistical Data Analysis in Python

subgd

Code for "Few-Shot Learning by Dimensionality Reduction in Gradient Space"

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

tcia_notebooks

Notebooks for working with The Cancer Imaging Archive datasets

tensorflow-programs-and-tutorials

Implementations of CNNs, RNNs, GANs, etc

tomophantom

Software to generate 2D/3D/4D analytical phantoms and their Radon transforms (parallel beam) for image processing

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

vmfnet

vMFNet: Compositionality Meets Domain-generalised Segmentation