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Nilearn Reviews & Product Details

Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data that leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

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Nilearn

Product Description

Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data that leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

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Nilearn Reviews (3)

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PA

Paresh A.

Software Engineer

Information Technology and Services

Enterprise (> 1000 emp.)

6/7/2018

"Best For Applying ML on NeuroImaging Data."

5/5

What do you like best about Nilearn?

Nilearn is the machine learning library developed especially for the neuroimaging data processing.It has vast trained models on the neuro imaging data gathered from various MRI machines and other neuro imaging machines.It can be used to apply supervised learning on neuroimaging data as well it can be used to suggest the treatment in accordance with the input data to predict the treatment.It can also be used for Decoding and MVPA.So it is the best library for applying Machine Learning on neuroimaging data and predict proper results. Review collected by and hosted on G2.com.

What do you dislike about Nilearn?

I have nothing to dislike about Nilearn because it has given best results for my application. Review collected by and hosted on G2.com.

Recommendations to others considering Nilearn:

I recommend using Nilearn because it helps you to predict best results on neuroimaging data and works better than any other API's so I would suggest using Nilearn if you are dealing with neuroimaging data or doing research on applying ML on neuroimaging data.Also if you are working to develop software for health sector dealing with neuro science than you should use Nilearn.In short if you are dealing with neuroimaging data I recommend you using Nilearn. Review collected by and hosted on G2.com.

What problems is Nilearn solving and how is that benefiting you?

I am a software developer and have to work with various sectors and develop softwares for them so I also get projects from health sector and for that I have to develop software for neurological doctor to predict the treatment in accordance with the imaging results and at that time I used Nilearn for the project.I also used it once for developing software for MRI developing company to integrate it with their machine.So Nilearn has helped us a lot. Review collected by and hosted on G2.com.

DP

Darshit P.

Senior Software Engineer

Information Technology and Services

Enterprise (> 1000 emp.)

6/5/2018

"Machine Learning for Neuro Imaging Data"

5/5

What do you like best about Nilearn?

Nilearn is the library for python which is used for neuro image processing.It makes easy for us to use many advanced machine learning,pattern recognition and multivariate statistical techniques on neuroimaging data.It can easily be used on fMRI data,resting data and VB data so it is the best api for neuro images.It is being used in the health sector for predicting clinical score or treatment response with supervised learning algorithms.It can also be used for many other functionalities for neuro imaging data.It is the best library for predicting and performing supervised learning on neuro imaging data. Review collected by and hosted on G2.com.

What do you dislike about Nilearn?

I have nothing to dislike about Nilearn because it is the best library which is being used in the health sector for predicting various responses. Review collected by and hosted on G2.com.

Recommendations to others considering Nilearn:

I recommend using Nilearn for applying supervised learning algorithms on the neuro imaging results produced from various imaging machines.If you are developing a software for health sector,you definetly require a machine learning algorithm to predict the treatment response for the doctor.So it is a very useful for us so I recommend using Nilearn for implementing machine learning for neuro imaging data and predict results accordingly. Review collected by and hosted on G2.com.

What problems is Nilearn solving and how is that benefiting you?

I am a software designer and once in a while we get some projects from health sector also.Recently we were working with the Imaging center and they required a software product for predicting various responses depending on the imaging of the machine in realtime.So we decided to use Nilearn for implementing neuro image predicting using supervised learning.So Nilearn helped us develop a software for that imaging center.I have also developed various softwares for doctors also which used it for predicting treatment responses based on the imaging results of MRI or CTScan.So Nilearn has been used many times by me. Review collected by and hosted on G2.com.

GL

Verified User in Law Practice

Small-Business (50 or fewer emp.)

1/16/2018

"Machine Learning for Neuro-Imaging"

2.5/5

What do you like best about Nilearn?

Nilearn makes it easy to use many advanced machine learning, pattern recognition and multivariate statistical techniques on neuroimaging data for applications such as MVPA (Mutli-Voxel Pattern Analysis), decoding, predictive modelling, functional connectivity, brain parcellations, connectomes.

Nilearn can readily be used on task fMRI, resting-state, or VBM data.

For a machine-learning expert, the value of nilearn can be seen as domain-specific feature engineering construction, that is, shaping neuroimaging data into a feature matrix well suited to statistical learning, or vice versa. Review collected by and hosted on G2.com.

What do you dislike about Nilearn?

There is no paper published yet about nilearn that reviewer knows of. Review collected by and hosted on G2.com.