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heudiconv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts.
NeuroImaging Workflows provides processing tools for magnetic resonance images of the brain.
This package provides processing pipelines for structural MRI.
Gifticlib is a a library for reading and writing files in GIfTI format. GIfTI is a standard for Geometry Data Format for Exchange of Surface-Based Brain Mapping Data.
The PETPVC toolbox comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data. Eight core PVC techniques are available, and those core methods can be combined to create a total of 22 different PVC techniques.
niworkflows is capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM).
The fsleyes-widgets package contains a collection of GUI widgets and utilities, based on wxPython, which are used by fsleyes-props and FSLeyes.
Nilearn enables approachable and versatile analyses of brain volumes and surfaces. It provides statistical and machine-learning tools, with instructive documentation & open community.
CiftiLib is a C++ library for CIFTI-2 file reading/writing. It additionally supports CIFTI-1 files, and supports both on-disk and in-memory access. It also provides C++ code for reading and writing generic NIfTI-1 and NIfTI-2 files.
CIFTI (Connectivity Informatics Technology Initiative) standardizes file formats for the storage of connectivity data. These formats are developed by the Human Connectome Project and other interested parties.
See http://www.nitrc.org/projects/cifti/ for more information.
pybids provides a set of tools for working with BIDS datasets. The BIDS standard aims at organizing and describing neuroimaging data in a uniform way in order to facilitate data sharing within the scientific community.
{dcmstack
This package provides utilities for feature analysis, preprocessing and visualization of image quality metrics generated by MRIQC.
NARPS Open Pipelines is a project aimed at reproducing the 70 pipelines from the NARPS study (Botvinik-Nezer et al., 2020) and sharing them as an open resource for the community. It uses Nipype for workflow management and provides templates to facilitate the reproduction of neuroimaging analyses.
This package provides tools for unsupervised and semi-supervised morphological segmentation.
Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the NLP and IR communities.
Extremely fast spelling checker and suggester in Python.
The following algorithms are supported currently:
Edit-distance
Editex
Soundex
Caverphone 1.0 and 2.0
Typox
All the above algorithms use an underlying Trie-based dictionary for efficient storage and fast computation.
Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.
This module can be used to extract or replace keywords in sentences, based on the FlashText algorithm.
WORLD Vocoder is a fast and high-quality vocoder which parameterizes speech into three components:
f0: Pitch contoursp: Harmonic spectral envelopeap: Aperiodic spectral envelope
It can also (re)synthesize speech using these features.
PyRuSH is the python implementation of RuSH, which is originally developed using Java. RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy.
The EDS-Pseudo project aims at detecting identifying entities in clinical documents, and was primarily tested on clinical reports at AP-HP's clinical data warehouse. The model is built on top of edsnlp, and consists in a hybrid model (rule-based + deep learning) for which we provide rules (eds-pseudo/pipes) and a training recipe. We also provide some fictitious templates and a script to generate a synthetic dataset.
This package provides Python bindings for the simstring text similarity matching library.
PyFastNER is the Python implementation of FastNER. It uses hash function to process multiple rules at the same time. Similar to FastNER, PyFastNER supports token-based rules and character-based rules.
This package provides a fast implementation of the Levenshtein distance with C++ and Cython.