Code for 2nd place solution to the 2017 National Data Science Bowl

Developing a well-documented repository for the Lung Nodule Detection task on the Luna16 dataset. This work is inspired by the ideas of the first-placed team at DSB2017, "grt123".

Data Science Bowl 2017

3d convnet for the classification of nodules/tumor in lung CT scans. Trained on Luna16 for Kaggle's 2017 data science bowl competition (result in top 3%)

An end-to-end walkthrough of the winning submission by grt123 for the Kaggle Data Science Bowl 2017

Data Science Bowl 2017 : Lung Cancer Detection

Kaggle Data Science Bowl 2017

Data Science Bowl 2017 Competition

This is a project based on Data Science Bowl 2017. I did my best to propose a solution for the problem but I am still new to Deep Learning so my solution is not the optimal one but it can definitely be improved with some fine tuning and better resources.

Kaggle Data Science Bowl 2017 - Detecting Lung Cancer From DICOM Scans

Gaussian Mixture Convolutional AutoEncoder applied to CT lung scans from the Kaggle Data Science Bowl 2017

Exploratory Analysis + Tutorials for kaggle Data Science Bowl 2017

Early detecting of lung cancer using the Luna data set with LIDC IDRI annotations using two models nodule classification"Googlent model" and the malignancy classification "Lenet model". This was for kaggle's Data science bowl 2017.

Algorithms for improving lung cancer detection with deep learning

Code for 8th place solution to Data Science Bowl 2017 Kaggle Competition

Training a 3D ConvNet to detect lung cancer from patient CT scans, while generating images of lung scans in real time. Adapted from 2017 Data Science Bowl

Canidadate for the Kaggle 2017 Data Science Bowl - Automatic detection of lung cancer from CT scans

Boost lung Cancer Detection using Generative model and Semi-Supervised Learning

Data Science Bowl 2017 for lung cancer prediction with Keras

This is a repository for a lung cancer detector trained on the training set for Data Science Bowl 2017

The Data Science Bowl 2017 23rd place

No description available

Kaggle Data Science Bowl 2017 solution

My crack at the Data Science Bowl Challenge 2017 using SPIE-AAPM Lung CT Dataset

Code for Kaggle Data Science Bowl 2017

kaggle data science bowl 2017 solution

https://www.kaggle.com/c/data-science-bowl-2017

Solution to Kaggle Competition: Data Science Bowl 2017

Experiments with processing the lung CT scans that are publicly available in the kaggle competition Data Science Bowl 2017. Evaluating different deep neural networks for training a model that helps early cancer detection.

Kaggle Data Science Bowl 2017