1. Automatic Lung Cancer Diagnosis from CT Scans (Week 1)
REU Student: Maria Jose Mosquera Chuquicusma
Graduate Student: Sarfaraz Hussein
Professor: Dr. Ulas Bagci

2. Background Lung cancer is the first cause of mortality among
all types of cancer.
- CT (Computed Tomography) Scans
CAD Systems (Computer Aided Diagnosis)
3D (sagittal, coronal, axial)
Save time
Avoid misdiagnosis, which could lead to
unnecessary surgeries, death, etc.
Validation and blind spots

3. Project Overview
- Design a CAD system to automatically diagnose lung nodules without supervision
- Techniques for extracting strong imaging features
- Use neural network architectures
Apply deep learning strategies
Find features extracted from other architectures that could help with lung nodule classification

4. Problem Overview Extensive training of supervised models
Time consuming
Limited labeled data
Regression accuracy not 100% yet

5. Tasks Accomplished
- Studied neural networks and machine learning techniques
- Familiarization with topic
Background reading of previous work:
1. TumorNET: Lung Nodule Characterization Using Multi-View Convolutional Neural
Network with Gaussian Process
2. Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
3. Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
- Understanding terminology
Techniques used to extract features and classify tumor nodules
Understanding how deep 3D CNNs work along with MTL and Transfer Learning
And much more... .

6. Background Reading 1: TumorNet
Architecture: Deep 3D CNN
Input: 3D-tensor vector
Training and testing: 10 fold cross validation
Data Augmentation
Dataset: LIDC- IDRI (1018 scans)
Gaussian noise with random mean, Poisson, Salt & Pepper and Speckle noise
GP to obtain malignancy score
Joint distribution  Conditional distribution
Latent function
Add Gaussian noise to latent function prevent overfitting in outputs
Covariance between testing and training sets .

7. Background Reading 1: TumorNet

8. Background Reading 2: 3D CNN-Based Multi-task Learning
- 3D CNN trained on Sports-1M dataset
- Dataset: LIDC-IDRI, 10 fold cross validation
- Transfer learning
fine-tune 3D CNNs using labels for malignancy and six attributes.
1 CNN per attribute
L1 norm
- Multi-task learning
Graph Regularized Sparse Representation
Trace norm
Correlation of different tasks: Coefficient Matrix W  Structure Matrix  Correlation Matrix  Binary Matrix
Scoring function
- Malignancy score

9. Background Reading 2: 3D CNN-Based Multi-task Learning

10. Background Reading 3: Hybrid Shape and Appearance Features
- Spherical Harmonics Computation and pre-trained DCNN
- 10 fold cross validation
- Two inputs:
Radiologists’ binary nodule segmentations  mesh representation  SH
functions  Vectors
Local CT image  Combine three orthogonal local patches (applied PCA on
binary segmentation data
- Extracted representation from SH method provides benefits (rotate, scale, transform, easier to compute)
- RF Classification
Synthesize DCNN and SH features
- Malignancy evaluation

11. Background Reading 3: Hybrid Shape and Appearance Features

12. Background Reading 3: Hybrid Shape and Appearance Features