73 citations · 305 across the 55 of their papers we have counts for
58 papers
FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model
Molin Zhang, Polina Golland, Patricia Ellen Grant +1
The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are…
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
Zeen Chi, Zhongxiao Cong, Clinton J. Wang +6
We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimati…
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
Yingcheng Liu, Neerav Karani, Neel Dey +5
The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effe…
Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation
Dominik Hollidt, Clinton Wang, Polina Golland +1
We present a novel approach to perform 3D semantic segmentation solely from 2D supervision by leveraging Neural Radiance Fields (NeRFs). By extracting features along a surface poin…
Interpolating between Images with Diffusion Models
Clinton J. Wang, Polina Golland
One little-explored frontier of image generation and editing is the task of interpolating between two input images, a feature missing from all currently deployed image generation p…
Boundary-weighted logit consistency improves calibration of segmentation networks
Neerav Karani, Neel Dey, Polina Golland
Neural network prediction probabilities and accuracy are often only weakly-correlated. Inherent label ambiguity in training data for image segmentation aggravates such miscalibrati…
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Neel Dey, S. Mazdak Abulnaga, Benjamin Billot +4
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structure…
Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI
Henry F. J. Tregidgo, Sonja Soskic, Mark D. Olchanyi +9
The human thalamus is a highly connected subcortical grey-matter structure within the brain. It comprises dozens of nuclei with different function and connectivity, which are affec…
Sample-Specific Debiasing for Better Image-Text Models
Peiqi Wang, Yingcheng Liu, Ching-Yun Ko +4
Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One…
Data Consistent Deep Rigid MRI Motion Correction
Nalini M. Singh, Neel Dey, Malte Hoffmann +5
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion…
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…
Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels
Sungmin Hong, Anna K. Bonkhoff, Andrew Hoopes +6
Despite the superior performance of Deep Learning (DL) on numerous segmentation tasks, the DL-based approaches are notoriously overconfident about their prediction with highly pola…
Volumetric Parameterization of the Placenta to a Flattened Template
S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev +3
We present a volumetric mesh-based algorithm for parameterizing the placenta to a flattened template to enable effective visualization of local anatomy and function. MRI shows pote…
Image Classification with Consistent Supporting Evidence
Peiqi Wang, Ruizhi Liao, Daniel Moyer +3
Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facil…
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images
Sungmin Hong, Razvan Marinescu, Adrian V. Dalca +4
Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as,…
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning
Junshen Xu, Esra Abaci Turk, P. Ellen Grant +2
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal functio…