In 2018, I got my master degree in the department of Computer Science,
Harbin Institute of Technology (HIT), China.
I am interested in Computer Vision, Deep Learning, and Artificial Intelligence.
I have done many interesting project during the past several years, especially about Human Pose Estimation, Object Detection, and Medical Image Processing.
Left Ventricular Volumes Prediction
This study aims to develop a new LV volumes prediction method without segmentation, motivated by deep learning technology and the large scale cardiac MRI (CMR) datasets from the second Annual Data Science Bowl (ADSB) in 2016. The experiments results shows that the predicted LV volumes have high correlation with the ground truth.
3D Left Ventricular Segmentation
We trained a convolutional neural network to generate a binary cuboid to locate the region of interest (ROI). And then, using ROI as the input, we trained stacked autoencoder to infer the LV initial shape. At last, we adopted snake model initiated by inferred shape to segment the LV endocardium.
Combined Deep Learning and Random Forests
The proposed method combined unsupervised multi-scale convolutional deep network and random forests. The multi-scale convolution deep network adopted multi-scale convolutional filters to represent features of unlabeled end-diastolic and end-systolic 3DE volumes (EDV and ESV). And then we formulated left ventricular volume estimation as a regression problem and used random forests for efficient volume estimation.
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