Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level …
Registering brain magnetic resonance imaging (MRI) scans containing pathologies is challenging primarily due to large deformations caused by the pathologies, leading to missing correspondences between scans. However, the registration task is …
Analyzing large-scale imaging studies with thousands of images is computationally expensive. To assess localized morphological differences, deformable image registration is a key tool. However, as registrations are costly to compute, large-scale …
We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. …
In this paper a novel Large Deformation Diffeomorphic Metric Mapping (LDDMM) scheme is presented which has significantly lower computational and memory demands than standard LDDMM but achieves the same accuracy. We exploit the smoothness of …
This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, …
Endoscopy enables high resolution visualization of tissue texture and is a critical step in many clinical workflows, including diagnosis and treatment planning for cancers in the nasopharynx. However, an endoscopic video does not provide 3D spatial …
Reliable estimation of model parameters from data requires a suitable model. In this work, we investigate and extend a recent model criticism approach to evaluate regression models on the Grassmann manifold. Model criticism allows us to check if a …
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014, held in conjunction with MICCAI 2014 in Boston, …
This paper presents an approach to estimate the uncertainty of registration parameters for the large displacement diffeomorphic metric mapping (LDDMM) registration framework. Assuming a local multivariate Gaussian distribution as an approximation for …