Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. In this talk, I will introduce the fundamentals of deep learning methods and their applications in computer-aided diagnosis for Alzheimer's Disease (AD), breast cancer, lung cancer, and brain tumors.
Highlights:
· Brief overview of deep learning
· Introduction of deep learning in Alzheimer's Disease (AD) diagnosis
· Applications in breast and lung cancer diagnosis
· Prediction of survival time for patients with brain tumors
Introduction:
Deep learning is an unsupervised method that can discover new features suitable for different applications. Although the conventional human-made filters can be used to extract certain advanced features, it is time-consuming to discover a new filter and also the extracted features may not fit a particular study under consideration. Besides, a lot of efforts need to spend on the testing and selection of different choices of human-made features, which is difficult for the researchers with limited experience to select suitable features. On the other hand, deep learning is designed to automatically discover features, from a set of given data, for each particular application. Therefore, it is able to discover new features that were never discovered by researchers before.
Deep learning has been applied to various problems in medical image analysis area, e.g., image segmentation, registration, and disease classification, all of which can be formulated as feature-matching problems and thus can be solved effectively with the learned new features by deep learning. In this talk, I will demonstrate the applications of deep learning in computer-aided diagnosis for brain disorders, breast cancer, lung cancer, and brain tumors.
More details about this talk can be found from papers below.
H.-I. Suk, S.-W. Lee, D. Shen, “Latent Feature Representation with Stacked Auto-Encoder for AD/MCI Diagnosis”, Brain Structure and Function, 2014.
H.-I. Suk, S.-W. Lee, D. Shen, “Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis”, NeuroImage, 2014.
R. Li, W. Zhang, H. Suk, L. Wang, J. Li, D. Shen, S. Ji, “Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis”, MICCAI, 2014.
J. Cheng, D. Ni, Y. Chou, J. Qin, C. Tiu, Y. Chang, D. Shen, C. Chen, “Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans”, Scientific Reports, 2016.
D. Nie, H. Zhang, E. Adeli, L. Liu, D. Shen. “3D Deep Learning for Multi-modal Imaging-guided Survival Time Prediction of Brain Tumor Patients”, MICCAI, 2016.
D. Shen, G. Wu, H.-I. Suk, “Deep Learning in Medical Image Analysis”, Annual Review of Biomedical Engineering, 2016.