Pinned Repositories
CLMRadiomics
Scripts to compute the features and develop the models from the paper "Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study .", M.P.A. Starmans, F. E. Buisman et al. 2021.
DMRadiomics
Scripts to compute the features and fit radiomics models as used in the paper "Differential diagnosis and mutation stratification of desmoid tumors on MRI using a radiomics approach." M. J. M. Timbergen, M. P. A. Starmans et al. 2020.
GISTRadiomics
Scripts to compute the features and fit radiomics models as used in the paper "Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach." M. P. A. Starmans, M. J. M. Timbergen et al..
LipoRadiomicsFeatures
Script to compute the features used in the paper: Vos, M., Starmans, M. P. A., Timbergen, M. J. M., van der Voort, S. R., Padmos, G. A., Kessels, W., ... & Visser J.J.. (2019). Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. The British journal of surgery, 106(13), 1800.
LiverRadiomics
Scripts to compute the radiomics features and fit the machine learning models as presented in the paper "Automated differentiation of malignant and benign primary solid liver lesions in non-cirrhotic livers on MRI: an externally validated radiomics model." by M. P. A. Starmans et al. 2021.
MelaRadiomics
Scripts to compute the radiomics features and fit the machine learning models as presented in the paper "The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning." by L. Angus and M. P. A. Starmans et al. 2021.
MesentericRadiomics
Scripts to compute the features and fit radiomics models as used in the paper: Blazevic, A., Starmans, M. P. A., Brabander, T., Dwarkasingh, R., van Gils, R., Hofland, J., Franssen, G. J., Feelders, R. A., Niessen, W. J., Klein, S., & de Herder, W. W. (2021). Predicting symptomatic mesenteric mass in neuroendocrine tumors using radiomics, Endocrine-Related Cancer, ERC-21-0064.
WORC
Workflow for Optimal Radiomics Classification
WORCDatabase
Code to reproduce the experiments as described in the paper "Reproducible radiomics through automated machine learning validated on twelve clinical applications", Starmans et al. 2021, In Preparation
WORCTutorial
Tutorial for the WORC Package
MStarmans91's Repositories
MStarmans91/WORC
Workflow for Optimal Radiomics Classification
MStarmans91/WORCTutorial
Tutorial for the WORC Package
MStarmans91/GISTRadiomics
Scripts to compute the features and fit radiomics models as used in the paper "Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach." M. P. A. Starmans, M. J. M. Timbergen et al..
MStarmans91/MelaRadiomics
Scripts to compute the radiomics features and fit the machine learning models as presented in the paper "The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning." by L. Angus and M. P. A. Starmans et al. 2021.
MStarmans91/WORCDatabase
Code to reproduce the experiments as described in the paper "Reproducible radiomics through automated machine learning validated on twelve clinical applications", Starmans et al. 2021, In Preparation
MStarmans91/CLMRadiomics
Scripts to compute the features and develop the models from the paper "Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study .", M.P.A. Starmans, F. E. Buisman et al. 2021.
MStarmans91/DMRadiomics
Scripts to compute the features and fit radiomics models as used in the paper "Differential diagnosis and mutation stratification of desmoid tumors on MRI using a radiomics approach." M. J. M. Timbergen, M. P. A. Starmans et al. 2020.
MStarmans91/LipoRadiomicsFeatures
Script to compute the features used in the paper: Vos, M., Starmans, M. P. A., Timbergen, M. J. M., van der Voort, S. R., Padmos, G. A., Kessels, W., ... & Visser J.J.. (2019). Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. The British journal of surgery, 106(13), 1800.
MStarmans91/LiverRadiomics
Scripts to compute the radiomics features and fit the machine learning models as presented in the paper "Automated differentiation of malignant and benign primary solid liver lesions in non-cirrhotic livers on MRI: an externally validated radiomics model." by M. P. A. Starmans et al. 2021.
MStarmans91/MesentericRadiomics
Scripts to compute the features and fit radiomics models as used in the paper: Blazevic, A., Starmans, M. P. A., Brabander, T., Dwarkasingh, R., van Gils, R., Hofland, J., Franssen, G. J., Feelders, R. A., Niessen, W. J., Klein, S., & de Herder, W. W. (2021). Predicting symptomatic mesenteric mass in neuroendocrine tumors using radiomics, Endocrine-Related Cancer, ERC-21-0064.
MStarmans91/AIPToolbox
Python toolbox for the Advanced Image Processing (AIP) masters course TM11005 in the clinical technology MSc of the Medical Delta.
MStarmans91/auto-sklearn
Automated Machine Learning with scikit-learn
MStarmans91/CirGuidanceRadiomics
Scripts to compute the radiomics features and fit the machine learning models as presented in the paper "Optimization of preoperative lymph node staging in patients with muscle-invasive bladder cancer using radiomics on computed tomography." by M. P. A. Starmans et al. 2021.
MStarmans91/ComBatHarmonization
Harmonization of multi-site imaging data with ComBat
MStarmans91/H-DenseUNet
TMI 2018. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
MStarmans91/hello-world
First repository
MStarmans91/lectures
repository with the lectures for MLSS Skoltech
MStarmans91/MStarmans91.github.io
Repository for my personal academic website
MStarmans91/multislice
Multi-slice CNN designed to classify heterogeneous medical images, in particular with large and variable slice thicknesses. The network can take any number of slices as input, thanks to a flexible architecture. Similar 2D and 3D CNNs are provided for comparison.
MStarmans91/pyradiomics
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.
MStarmans91/Radiomics-GPU-Server
MStarmans91/SMAC3WORC
Sequential Model-based Algorithm Configuration
MStarmans91/starter-hugo-academic
🎓 Hugo Academic Theme 创建一个学术网站. Easily create a beautiful academic résumé or educational website using Hugo, GitHub, and Netlify.
MStarmans91/TM10007_PROJECT
MStarmans91/tutorials
repository with the tutorials for MLSS Skoltech
MStarmans91/tutorials_week2
tutorials for MLSS 2019 Skoltech