73 citations · 305 across the 55 of their papers we have counts for
9 papers · 1 filter
Using Multiple Instance Learning to Build Multimodal Representations
Peiqi Wang, William M. Wells, Seth Berkowitz +2
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-moda…
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets
Deborah Pereg, Martin Villiger, Brett Bouma +1
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset. The learner is…
Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging
Vivek Gopalakrishnan, Polina Golland
The use of digitally reconstructed radiographs (DRRs) to solve inverse problems such as slice-to-volume registration and 3D reconstruction is well-studied in preoperative settings.…
Automatic Segmentation of the Placenta in BOLD MRI Time Series
S. Mazdak Abulnaga, Sean I. Young, Katherine Hobgood +5
Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Mea…
RadTex: Learning Efficient Radiograph Representations from Text Reports
Keegan Quigley, Miriam Cha, Ruizhi Liao +4
Automated analysis of chest radiography using deep learning has tremendous potential to enhance the clinical diagnosis of diseases in patients. However, deep learning models typica…
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
Junshen Xu, Daniel Moyer, P. Ellen Grant +3
Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task…
Discretization Invariant Networks for Learning Maps between Neural Fields
Clinton J. Wang, Polina Golland
With the emergence of powerful representations of continuous data in the form of neural fields, there is a need for discretization invariant learning: an approach for learning maps…
Supervision by Denoising for Medical Image Segmentation
Sean I. Young, Adrian V. Dalca, Enzo Ferrante +4
Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging doma…
Symmetric Volume Maps: Order-Invariant Volumetric Mesh Correspondence with Free Boundary
S. Mazdak Abulnaga, Oded Stein, Polina Golland +1
Although shape correspondence is a central problem in geometry processing, most methods for this task apply only to two-dimensional surfaces. The neglected task of volumetric corre…