AIM-Harvard · donglihe-hub · Nov 18, 2025 · Nov 28, 2025 · Dec 3, 2025 · Dec 3, 2025
diff --git a/.gitignore b/.gitignore
@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+*code-workspace
 
 # C extensions
 *.so
@@ -165,3 +166,5 @@ Thumbs.db
 
 # Ignore test outputs
 data/*.csv
+
+.vscode
diff --git a/README.md b/README.md
@@ -1,209 +1,12 @@
-# pyradiomics v3.1.0
+# pyradiomics-torch
 
-\<-- ## Build Status
+New features compared to the pyradiomics library:
 
-| Linux / MacOS | Windows |
-| ------------- | ------- |
-|               |         |
-| -->           |         |
+1. GPU-accerlerated computation using PyTorch
+2. Online computation of radiomics features during training/inference
 
-## Radiomics feature extraction in Python
+PS:
+I'm not familiar with C so I rewrote the C codes in Python :(
 
-This is an open-source python package for the extraction of Radiomics features
-from medical imaging.
-
-With this package we aim to establish a reference standard for Radiomic
-Analysis, and provide a tested and maintained open-source platform for easy and
-reproducible Radiomic Feature extraction. By doing so, we hope to increase
-awareness of radiomic capabilities and expand the community.
-
-The platform supports both the feature extraction in 2D and 3D and can be used
-to calculate single values per feature for a region of interest
-("segment-based") or to generate feature maps ("voxel-based").
-
-**Not intended for clinical use.**
-
-**If you publish any work which uses this package, please cite the following
-publication:** _van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A.,
-Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillion-Robin, J. C., Pieper, S.,
-Aerts, H. J. W. L. (2017). Computational Radiomics System to Decode the
-Radiographic Phenotype. Cancer Research, 77(21), e104–e107.
-https://doi.org/10.1158/0008-5472.CAN-17-0339_
-
-### Join the Community!
-
-Please join the
-[Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).
-
-### Feature Classes
-
-Currently supports the following feature classes:
-
-- First Order Statistics
-- Shape-based (2D and 3D)
-- Gray Level Co-occurrence Matrix (GLCM)
-- Gray Level Run Length Matrix (GLRLM)
-- Gray Level Size Zone Matrix (GLSZM)
-- Gray Level Dependence Matrix (GLDM)
-- Neighboring Gray Tone Difference Matrix (NGTDM)
-
-### Filter Classes
-
-Aside from the feature classes, there are also some built-in optional filters:
-
-- Laplacian of Gaussian (LoG, based on SimpleITK functionality)
-- Wavelet (using the PyWavelets package)
-- Square
-- Square Root
-- Logarithm
-- Exponential
-- Gradient (Magnitude)
-- Local Binary Pattern (LBP) 2D / 3D
-
-### Supporting reproducible extraction
-
-Aside from calculating features, the pyradiomics package includes provenance
-information in the output. This information contains information on used image
-and mask, as well as applied settings and filters, thereby enabling fully
-reproducible feature extraction.
-
-### Documentation
-
-For more information, see the sphinx generated documentation available
-[here](http://pyradiomics.readthedocs.io/).
-
-Alternatively, you can generate the documentation by checking out the master
-branch and running from the root directory:
-
-```
-sphinx-build docs docs/_build/
-```
-
-The documentation can then be viewed in a browser by opening
-`PACKAGE_ROOT\build\sphinx\html\index.html`.
-
-Furthermore, an instruction video is available
-[here](http://radiomics.io/pyradiomics.html).
-
-### Installation
-
-PyRadiomics is OS independent and compatible with Python >= 3.5. Pre-built
-binaries are available on PyPi and Conda. To install PyRadiomics, ensure you
-have python installed and run:
-
-```
-`python -m pip install pyradiomics`
-```
-
-Detailed installation instructions, as well as instructions for building
-PyRadiomics from source, are available in the
-[documentation](http://pyradiomics.readthedocs.io/en/latest/installation.html).
-
-### Docker
-
-PyRadiomics also supports [Dockers](https://www.docker.com/). Currently, 2
-dockers are available:
-
-The first one is a [Jupyter notebook](http://jupyter.org/) with PyRadiomics
-pre-installed with example Notebooks.
-
-To get the Docker:
-
-```
-docker pull radiomics/pyradiomics:latest
-```
-
-The `radiomics/notebook` Docker has an exposed volume (`/data`) that can be
-mapped to the host system directory. For example, to mount the current
-directory:
-
-```
-docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook
-```
-
-or for a less secure notebook, skip the randomly generated token
-
-```
-docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook start-notebook.sh --NotebookApp.token=''
-```
-
-and open the local webpage at http://localhost:8888/ with the current directory
-at http://localhost:8888/tree/data.
-
-The second is a docker which exposes the PyRadiomics CLI interface. To get the
-CLI-Docker:
-
-```
-docker pull radiomics/pyradiomics:CLI
-```
-
-You can then use the PyRadiomics CLI as follows:
-
-```
-docker run radiomics/pyradiomics:CLI --help
-```
-
-For more information on using docker, see
-[here](https://pyradiomics.readthedocs.io/en/latest/installation.html#use-pyradiomics-docker)
-
-### Usage
-
-PyRadiomics can be easily used in a Python script through the `featureextractor`
-module. Furthermore, PyRadiomics provides a commandline script, `pyradiomics`,
-for both single image extraction and batchprocessing. Finally, a convenient
-front-end interface is provided as the 'Radiomics' extension for 3D Slicer,
-available [here](https://github.com/AIM-Harvard/SlicerRadiomics).
-
-### 3rd-party packages used in pyradiomics:
-
-- SimpleITK (Image loading and preprocessing)
-- numpy (Feature calculation)
-- PyWavelets (Wavelet filter)
-- pykwalify (Enabling yaml parameters file checking)
-- scipy (Only for LBP filter, install separately to enable this filter)
-- scikit-image (Only for LBP filter, install separately to enable this filter)
-- trimesh (Only for LBP filter, install separately to enable this filter)
-
-See also the requirements section of the [pyproject file](pyproject.toml).
-
-### 3D Slicer
-
-PyRadiomics is also available as an
-[extension](https://github.com/AIM-Harvard/SlicerRadiomics) to
-[3D Slicer](slicer.org). Download and install the 3D slicer
-[nightly build](http://download.slicer.org/), the extension is then available in
-the extension manager under "SlicerRadiomics".
-
-### License
-
-This package is covered by the open source [3-clause BSD License](LICENSE.txt).
-
-### Developers
-
-- [Joost van Griethuysen](https://github.com/JoostJM)<sup>1,3,4</sup>
-- [Andriy Fedorov](https://github.com/fedorov)<sup>2</sup>
-- [Nicole Aucoin](https://github.com/naucoin)<sup>2</sup>
-- [Jean-Christophe Fillion-Robin](https://github.com/jcfr)<sup>5</sup>
-- [Ahmed Hosny](https://github.com/ahmedhosny)<sup>1</sup>
-- [Steve Pieper](https://github.com/pieper)<sup>6</sup>
-- [Hugo Aerts (PI)](https://github.com/hugoaerts)<sup>1,2</sup>
-
-<sup>1</sup>Department of Radiation Oncology, Dana-Farber Cancer Institute,
-Brigham and Women's Hospital, Harvard Medical School, Boston, MA,
-<sup>2</sup>Department of Radiology, Brigham and Women's Hospital, Harvard
-Medical School, Boston, MA, <sup>3</sup>Department of Radiology, Netherlands
-Cancer Institute, Amsterdam, The Netherlands, <sup>4</sup>GROW-School for
-Oncology and Developmental Biology, Maastricht University Medical Center,
-Maastricht, The Netherlands, <sup>5</sup>Kitware, <sup>6</sup>Isomics
-
-### Contact
-
-We are happy to help you with any questions. Please contact us on the
-[Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).
-
-We welcome contributions to PyRadiomics. Please read the
-[contributing guidelines](CONTRIBUTING.rst) on how to contribute to PyRadiomics.
-
-**This work was supported in part by the US National Cancer Institute grants:
-U24CA194354 - QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE and
-U01CA190234 - TUMOR GENOTYPE AND RADIOMIC PHENOTYPE IN LUNG CANCER**
+# Limited Support
+Only basic functionalities are supported, i.e., default parameters.
diff --git a/playground.ipynb b/playground.ipynb
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d7676215",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "Path(\".\").is_symlink()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fe9626e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pdac",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,11 +1,10 @@
-#NOTE: numpy 2.1 is only supported by 3.10 and greater
 [build-system]
 requires = ["scikit-build-core>=0.11.2", "numpy>=2.0"]
 build-backend = "scikit_build_core.build"
 
 
 [project]
-name = "pyradiomics"
+name = "pyradiomics-torch"
 authors = [
     { name = "PyRadiomics Community", email = "pyradiomics@googlegroups.com"}
 ]
@@ -34,7 +33,8 @@ dependencies = [
     "numpy>=2.0",
     "SimpleITK>=2.4.0",
     "PyWavelets >= 1.6.0",
-    "pykwalify >= 1.6.0"
+    "pykwalify >= 1.6.0",
+    "torch>=2.0"
 ]
 
 [project.optional-dependencies]