Author Archives: qianwang

Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis

One of the most popular trends in medical image analysis is to apply deep learning techniques to computer-aided diagnosis (CAD). However, it is evident now that a large number of manually labeled data is often a must to train a properly functioning deep network. This demand for supervision data and labels is a major bottleneck, since collecting a large number of annotations from experienced experts can be time-consuming and expensive.

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Unsupervised Landmark Detection Based Motion Estimation for Dynamic Medical Images

Dynamic medical imaging in 4D typically requires motion estimation of the organs. With respect to the spatial-temporal information of the 3D volume sampled over multiple time points, one may assess structural and functional property of the target organ. Our early study in CVPR 2020 shows that, with limited number of temporally sampled phase images, one can reconstruct the organ motion trajectory of high resolution both spatially and temporally, via interpolation in a well-encoded latent space.

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