Automatic Lung Cancer Diagnosis from CT Scans (Week 2) REU Student: Maria Jose Mosquera Chuquicusma Graduate Student: Sarfaraz Hussein Professor: Dr. Ulas Bagci

Tasks Accomplished Unsupervised Learning Tutorial: Familiarization with unsupervised learning terminologies such as: Bayes rule & Naïve Bayes (MAP & ML parameter estimates) Latent variable models (Factor analysis, PCA, ICA, Mixture of Gaussians, K-means clustering) EM Algorithm Modelling time series and other structured data (SSMs, HMMs) Intractability Graph models (undirected graphs, factor graphs, direct graphs) Etc. Literatures: 1. Discriminative Unsupervised Feature Learning with Convolutional Neural Networks 2. Early Visual Concept Learning with Unsupervised Deep Learning 3. Towards Automatic Pulmonary Nodule Management in Lung Cancer Screening with Deep Learning 4. Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. 5. Multi-crop CNNs for lung nodule malignancy suspiciousness classification 6. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks 7. Representation Learning: A Review and New Perspectives (in progress) 8. Why Does Unsupervised Pre-training Help Deep Learning? (in progress) 9. Unsupervised Learning Tutorial

1: Discriminative Unsupervised Feature Learning with CNNs Concentrates on concepts of invariance and ability to discriminate CNN with no labeled data Surrogate classes (sets) Class created by taking a sample patch image and applying transformations it Train network to discriminate among them .

1: Discriminative Unsupervised Feature Learning with CNNs

2: Early Visual Concept Learning with Unsupervised Deep Learning Newborns are exposed to transformation of objects Achieve zero-shot inference Variational Autoencoder Each latent factor (disentangled) learns to encode transformations

3: Towards automatic pulmonary management in lung cancer screening with deep learning

3: Towards automatic pulmonary management in lung cancer screening with deep learning Multi-stream multi-scale architecture CNNs process multiple triplets (sagittal, coronal, axial) of 2D views of a nodule at multiple scales Gives probability for each class Avoids nodule segmentation Data augmentation Observers vs. Computer

3: Towards automatic pulmonary management in lung cancer screening with deep learning

4: Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring Convolutional Sparse Autoencoder (CSAE) Task 1: Automated segmentation of breast density (MD) Task 2: Characterize mammographic textural (MT) patterns Convolutional layers are trained as autoencoders Pre-training Fine-tuning .

4: Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

5: Multi-crop CNNs for Lung Nodule Malignancy Suspiciousness Classification Multiple networks involves more computational costs Multi-crop pooling layer (cropping feature maps): surrogates standard pooling Malignancy suspiciousness classification using CT imaging Problems: Nodule delineation due to morphology variation and capturing quantitative characteristics from nodule (heterogeneity) Data augmentation

5: Multi-crop CNNs for Lung Nodule Malignancy Suspiciousness Classification

5: Multi-crop CNNs for Lung Nodule Malignancy Suspiciousness Classification

6: Unsupervised Representation Learning with DCGANs Deep Convolutional GANs Use an all convolutional net (no pooling or fully connected layers) Generator: Pooling replaced with fractional strided convolutions Discriminator: Pooling replaced with strided convolutions (helps downsampling) Batchnorm on both  helps with normalizing  efficient learning ReLU activation in all layers (generator) except output (Tanh)  network learns quicker LeakyReLU activation in all layers (discriminator) Deduplication test in case of memorization Generative models could model object attributes like scale, rotation, and position (less data required)

6: Unsupervised Representation Learning with Deep Convolutional GANs