2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification

Home < 2017 Winter Project Week < Evaluate Deep Learning for binary cancer legion classification

Key Investigators

Objective	Approach and Plan	Progress and Next Steps
Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images	Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier. The dataset consists of a series of 52x52 Region Of Interest T2 MR images containing cancer lesions and two T2 image series which do not contain lesions. We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.	created a DIGITS Amazon instance using NVIDIA's marketplace image before project week Prepared the dataset in the style of the MNIST example Trained LeNet and AlexNet CNNs using DIGITS interface and Caffe learning framework Data augmentation was crucial to improve results up to 83% detection accuracy for 2D case 3D data was presented without augmentation and yielded better results than 2D alone We believe results will further improve when better data augmentation and 3D slice data are used simultaneously