Search Results for author: Lawrence Staib

Found 10 papers, 3 papers with code

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

no code implementations6 Apr 2023 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan

Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation.

Image Segmentation Medical Image Segmentation +2

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

1 code implementation5 Apr 2023 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jasjeet S. Sekhon, James S. Duncan

In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.

Contrastive Learning Image Segmentation +2

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

no code implementations27 Sep 2022 Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Haoran Su, Xiaoran Zhang, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan

Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.

Anatomy Contrastive Learning +3

Learning correspondences of cardiac motion from images using biomechanics-informed modeling

1 code implementation1 Sep 2022 Xiaoran Zhang, Chenyu You, Shawn Ahn, Juntang Zhuang, Lawrence Staib, James Duncan

Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures.

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

no code implementations3 Jun 2022 Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey, Lawrence Staib, James S. Duncan

Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.

Image Segmentation Incremental Learning +3

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

no code implementations13 Aug 2021 Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan

However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.

Data Augmentation Image Generation +4

Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

no code implementations14 May 2021 Chenyu You, Ruihan Zhao, Lawrence Staib, James S. Duncan

In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.

Contrastive Learning Image Segmentation +4

Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results

1 code implementation16 Jan 2020 Xiaoxiao Li, Yufeng Gu, Nicha Dvornek, Lawrence Staib, Pamela Ventola, James S. Duncan

However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required.

Domain Adaptation Federated Learning +2

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