• Publications
  • Influence
An Unsupervised Learning Model for Deformable Medical Image Registration
TLDR
The proposed method uses a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field, and demonstrates registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice.
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
TLDR
VoxelMorph promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications and demonstrates that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster.
Synthesizing Images of Humans in Unseen Poses
TLDR
A modular generative neural network is presented that synthesizes unseen poses using training pairs of images and poses taken from human action videos, separates a scene into different body part and background layers, moves body parts to new locations and refines their appearances, and composites the new foreground with a hole-filled background.
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation
TLDR
This work learns a model of transformations from the images, and uses the model along with the labeled example to synthesize additional labeled examples, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures.
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions
TLDR
The method can recover human gait videos and face images from spatial projections, and then show that it can recover videos of moving digits from dramatically motion-blurred images obtained via temporal projection.
A Video-Based Method for Automatically Rating Ataxia
TLDR
The feasibility of using computer vision and machine learning to produce consistent and clinically useful measures of motor impairment in patients with ataxia is demonstrated.
WiFi Motion Detection: A Study into Efficacy and Classification
  • Sadhana Lolla, Amy Zhao
  • Computer Science, Engineering
    IEEE Integrated STEM Education Conference (ISEC)
  • 1 March 2019
TLDR
A motion detection system that utilizes WiFi Channel State Information (CSI), which describes how a wireless signal propagates from the transmitter to the receiver, and supervised machine learning algorithms to classify a set of simple motions using a proposed feature extraction methods.
Estimating a Small Signal in the Presence of Large Noise
TLDR
A preliminary algorithm for estimating the signal amplitude in the presence of relatively high noise is presented and it is demonstrated that the algorithm can be used to accurately estimate the signals amplitude in an uncompressed simulated video, but is susceptible to compression noise and motion.
A Video-Based Method for Objectively Rating Ataxia
TLDR
The feasibility of using computer vision and machine learning to produce consistent and clinically useful measures of motor impairment is demonstrated, using videos of the finger-to-nose test.
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