Automatic Lung Cancer Diagnosis from CT Scans (Week 3) REU Student: Maria Jose Mosquera Chuquicusma Graduate Student: Sarfaraz Hussein Professor: Dr. Ulas Bagci

Tasks Accomplished Programming/Other: Familiarization with MATLAB and its Pre-Trained Neural Networks Familiarization with ITK-SNAP Software Literatures: 1. Representation Learning: A Review and New Perspectives 2. Why Does Unsupervised Pre-training Help Deep Learning? 3. Multiview Convolutional Neural Network for Lung Nodule Classification 4. Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images 5. Pulmonary Nodule Classification with Deep Neural Networks on Computed Tomography Images 6. An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax

1: Representation Learning: A Review and New Perspectives General purpose priors for good representations Deep Representations PCA/ICA Markov Random Fields Bayesian Networks Restricted Boltzman Machines KL Divergence, Contrastive Divergence Different types of Autoencoders (sparse, regularized, stacked, contracted) Deep Belief Networks Etc.

2: Why Does Unsupervised Pre-training Help Deep Learning? Pre-trained weights gathered through unsupervised learning are more beneficial then setting random weights Better generalization Training cost optimized  better regularization  less overfitting Great for large layers and deeper networks; it can hurt smaller networks Training and testing errors were lower Act as a regularizer (maintained) Advantage does not vanish as the number of samples increases Variance reduction Pre-train more layers  generalization

3: Multiview Convolutional Neural Network for Lung Nodule Classification Multiview CNN for classification of pulmonary nodules (malignant or benign) Motivation: Multiple view provide different information 2 Tasks: binary and ternary classifications Experiments performed for both tasks with 2 different input channel modes (1 and 7) and 5 different architecture with Batch Normalization Batch Normalization used for faster learning and higher overall accuracy (but caused overfitting) Deeper Network  separability of classes Sensitivity, Specificity, ROC, t-SNE

3: Multiview Convolutional Neural Network for Lung Nodule Classification

3: Multiview Convolutional Neural Network for Lung Nodule Classification

4: Multi-Stage Neural Networks with Single-Sided Classifiers Cascaded multi-stage CNN with single-sided classifiers Reduce the positives of lung nodule classification in CT scans Irregular lesions in non-nodules create an imbalance Inverse balanced dataset used CNN stage filtering for non-nodules Suspicious and obvious non-nodules Threshold Last CNN calculates probabilities

4: Multi-Stage Neural Networks with Single-Sided Classifiers

4: Multi-Stage Neural Networks with Single-Sided Classifiers

5: Pulmonary Nodule Classification with Deep Neural Networks on Computed Tomography Images Deep Convolutional Neural Network Motivation: Diminish and ultimately reduce false positives and false negatives Classify whether ROIs contain a nodule or non-nodule 2 Tasks: binary and ternary classifications Experiments performed where tested CF and DD Adjusted learning rate and momentum of weight updating 5 different trainings: training 4 gave most optimal results

5: Pulmonary Nodule Classification with Deep Neural Networks on Computed Tomography Images

6: An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax Shannon’s Information Theory (closely related to MI and MAP) Received signal ~ transmitted signal + noise Monte Carlo (MC) sampling Markov Chains 𝑋 →𝑌 → 𝑌 → 𝑌 →𝑅 Want to establish a relationship between 𝑋 and 𝑅

6: An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax