mstarmans91 Goto Github PK
Name: Martijn P. A. Starmans
Type: User
Bio: PostDoc AI for medical imaging @ Erasmus Medical Center
Name: Martijn P. A. Starmans
Type: User
Bio: PostDoc AI for medical imaging @ Erasmus Medical Center
Python toolbox for the Advanced Image Processing (AIP) masters course TM11005 in the clinical technology MSc of the Medical Delta.
Automated Machine Learning with scikit-learn
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.
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.
Harmonization of multi-site imaging data with ComBat
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.
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..
TMI 2018. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
First repository
repository with the lectures for MLSS Skoltech
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.
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.
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.
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.
Repository for my personal academic website
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.
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.
Sequential Model-based Algorithm Configuration
🎓 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.
repository with the tutorials for MLSS Skoltech
tutorials for MLSS 2019 Skoltech
Workflow for Optimal Radiomics Classification
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
Tutorial for the WORC Package
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Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.