Automated Lung Nodule Detection using Deep Neural Networks
With a better lung nodule detection system, we could point to clinicians images requiring further review and reduce the rate of human error in cancer diagnosis.
Specific Aim 1. To build a normalization method that accounts for batch effects.
To address batch effects and other artifacts, we will build a normalization method that makes the images comparable.
Specific Aim 2. To identify lung nodules systematically
We will train deep neural networks to identify possible lung nodules from the CT scans.
Specific Aim 3. To classify benign and malignant lung tumors
We will label the lung tumors by reviewing the scans manually and build an automated nodule classification system using convolutional neural networks.
Specific Aim 4. To reveal the radiology-pathology association via machine learning
Patients with different pathology subtypes may present different radiology manifestations on their CT scan images. We will develop machine learning algorithms to associate radiology and pathology imaging findings.
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