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+# Survival-Prediction
+This repository presents our code for the 2020 challenge "Predicting lung cancer survival time" (proposed by Owkin and hosted by ENS - Collège de France), done jointly with Léon Zheng. We took part to this challenge as part of the Multi-scale models and convolutional neural networks MVA course (teached by Stéphane Mallat). Our work is summarized in Report.pdf. 
+
+We finished first on the private leaderboard among the 98 teams that participated (https://challengedata.ens.fr/participants/challenges/33/), with a C-index of 77.47. Our best solution relies on feature selection with LASSO regression using a combination of features (pyradiomics features including provided ones and additional ones, Laplacian of Gaussian features and wavelet features), plus a 3D U-Net trained for binary semantic segmentation to recover corrupted masks upstream of extraction.
+
+## Feature extraction
+Extraction of more pyradiomics features (~130 instead of the ~50 provided), Laplacian of Gaussian and wavelet features: see notebook Feature_extraction/Feature_Extraction_CT.ipynb.
+
+## Feature Selection
+We tried 3 different feature selection procedures:
+- Recursive Feature Elimination
+- LASSO regression: see notebook lasso/FeatureSelectionLasso.ipynb
+- Random survival forest: see notebook rsf/rsf_feature_selection.ipynb
+
+## Computer Vision approach
+The following functionalities aim to provide solutions to the prediction of survival time from images only with a deep learning based approach - without relying on pyradiomics features - and are provided in the notebook ScanCNN/ScanCNN.ipynb:
+- Visualization of the tumor in various forms: video, 2D slice, 3D shape
+- Simplification to a 2D problem with extraction of the 2D "most discriminative" slice and feature extraction with a pretrained ResNet-18 model
+- Simplification to binary classification between patients that have high risks or low risks
+- End-to-end pipeline with negative log partial likelihood to finetune a ResNet-18 while learning a 3-layer perceptron or an attention-based model predicting the risk of patients on top of it
+- Analysis of these approaches with Grad-Cam
+- Binary Image Semantic Segmentation with a 3D U-Net to recover missing masks and for 3D feature extraction
+
+One approach that could be interesting to extend this work would consist in using 3D models pretrained on additional data like https://github.com/Tencent/MedicalNet.