LIDC-IDRI

by RaulMedeiros Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | LIDC-IDRI Summary

LIDC-IDRI is a Python library. LIDC-IDRI has no bugs, it has no vulnerabilities and it has low support. However LIDC-IDRI build file is not available. You can download it from GitHub.

LIDC-IDRI

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Quality

Security

License

Reuse

Support

LIDC-IDRI has a low active ecosystem.

It has 5 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

LIDC-IDRI has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of LIDC-IDRI is current.

Quality

LIDC-IDRI has no bugs reported.

Security

LIDC-IDRI has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

LIDC-IDRI does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

LIDC-IDRI releases are not available. You will need to build from source code and install.

LIDC-IDRI has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed LIDC-IDRI and discovered the below as its top functions. This is intended to give you an instant insight into LIDC-IDRI implemented functionality, and help decide if they suit your requirements.

Extract Nodules Centroids from a folder
This function parses an XML file into a list of Params
Converts an exam folder to Matlab
Load nodule voxels from unblinded reads
This function processes the folder s annotation files
Merge all noduleids
Exports all nodules
Compute the ROI
Plot a confusion matrix
Train the model
Loads all nodule files
Duplicate the given class
Simplify x and y
Shuffle the data
Extract deep features from an image
Load image
Plot the model accuracy
Creates a model from the given model name
Plot confusion matrix
Load a model from disk
Logs training metrics
Slice nodule images
Saves a model to disk
Freeze the model
Log final model evaluation
Extract features from the input tensor

Get all kandi verified functions for this library.

LIDC-IDRI Key Features

No Key Features are available at this moment for LIDC-IDRI.

LIDC-IDRI Examples and Code Snippets

No Code Snippets are available at this moment for LIDC-IDRI.

Community Discussions

Trending Discussions on LIDC-IDRI

What are differences between 1) training a CNN from whole training set and 2) training from a subset of training set and then whole training set?

QUESTION

What are differences between 1) training a CNN from whole training set and 2) training from a subset of training set and then whole training set?

Asked 2017-Aug-12 at 11:08

I am working on training a segmentation network U-net on the LIDC-IDRI dataset. There are currently two training strategies:

Train the model on the whole training set from scratch (40k steps, 180k steps).
Train the model on 10% of the whole training set. After convergence (30k steps), continue to train the model on the whole training set (10k steps).

With Dice coefficient as loss function, which is also used in V-net architecture (paper), model trained with Method 2 is always better than that with Method 1. The former can achieve a Dice score of 0.735, while the latter can only reach 0.71.

BTW, my U-net model is implemented in TensorFlow, and the model is trained on NVidia GTX 1080Ti

Could anyone give some explanation or references. Thanks!

...

ANSWER

Answered 2017-Aug-12 at 11:08

Well, I read your answer and decided to try it, as it was fairly easy, as I've also been training Vnets on LIDC-IDRI. Usually I train on the whole dataset from the beginning. Option 2) gave faster boost in dice, however, soon it fell to 2% on validation and even after enabling the network to learn the whole dataset it did not recover, training dice. of course, was increasing. Seems my 10% of dataset were not quite representative and it badly overfit.

Source https://stackoverflow.com/questions/44977199

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install LIDC-IDRI

You can download it from GitHub.
You can use LIDC-IDRI like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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