DeformTTA: Morphology-Aware Controllable Deformation for Test-Time Adaptation in Fetal Cardiac Ultrasound Image Segmentation
Authors:
- 25 September 2026
- book chapter
- Published by Springer Nature
- p. 486-495
Abstract
No abstract availableKeywords
This publication has 22 references indexed in Scilit:
- Anti-forgetting Test-Time Adaptation for Robust Medical Image Analysis Under Distribution Shift2025
- Training-Free Test-Time Improvement for Explainable Medical Image Classification2025
- Leveraging Diffusion Models for Continual Test-Time Adaptation in Fundus Image Classification2025
- Single Image Test-Time Adaptation via Multi-View Co-Training2025
- TEGDA: Test-Time Evaluation-Guided Dynamic Adaptation for Medical Image Segmentation2025
- Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentationMedical Image Analysis, 2025
- IterMask3D: Unsupervised anomaly detection and segmentation with test-time iterative mask refinement in 3D brain MRIMedical Image Analysis, 2025
- Segment Anything in Medical Images with nnUNet2025
- Weakly-supervised convolutional neural networks for multimodal image registrationMedical Image Analysis, 2018
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015