dilated-cnn

We propose a pipeline for lung tumor detection and segmentation on the NSCLC Radiomics dataset. The pipeline utilized a hybrid-3d dilated convolutional neural network architecture for the segmentation task and won the IEEE VIP Cup 2018 challenge.

Experimented the effectiveness of 1D Convolutional Neural Networks & 2D Dilated Convolutional Neural Networks on classifying diseases from the TCGA pan-cancer dataset. Our proposed methodology produced 95.6% accuracy over the TCGA RNASeq dataset.