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Lung Cancer Data Science Bowl 2017 - GitHub Repository for the Vila del Pingui team for the Data Science Bowl 2017 (Feb2017 to Apr2017) The competetition ($1M in prizes) was about predicting early stage lung cancer from CT Scan images
Deep Convolutional Neural Networks for Lung Cancer Detection Here we demonstrate a CAD system for lung cancer clas-sification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl 2017 Thresholding was used as an initial segmentation approach to to segment out lung tissue from the rest of the CT scan
GitHub - owkin DSB2017: Data Science Bowl 2017 : Lung Cancer Detection We developed two methods, both based on nodule detection using the LUNA dataset Both methods pre-process the images to get a fixed 1mm x 1mm x 1mm resolution and segment the lungs using thresholding, morphological operations and connected components selection
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data . . . Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and nally assigns a cancer probability based on these results We discuss the challenges and advantages of our framework In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams positive rate in diagnosis
DSB17 3d lung nodule classifier - GitHub This repository contains the first stage of my solution for the 2017 Kaggle data science bowl (ranked in the top 3%) It consists in a 3d convnet for the classification of lung proposed tissue regions for nodules tumor in lung CT scans
Kaggle Data Science Bowl 2017: Detecting Lung Cancer The objective was to detect lung cancer based on CT scans of the chest from individuals diagnosed with cancer within a year Despite their lack of specific knowledge in medical image analysis or cancer prediction, the team secured 9th place in the competition
GitHub - anlthms dsb-2017: Data Science Bowl 2017 This code is for training a 3D Convolutional Neural Network on the LUNA16 dataset in order to detect malignant nodules I am hopeful that this can be used as the first step towards solving the DSB 2017 challenge