Anatomically-aware conformal prediction for medical image segmentation with random walks
Fuente:
arXiv
Saved in:
| Main Authors: | Gaillochet, Mélanie, Desrosiers, Christian, Lombaert, Hervé |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Prompt learning with bounding box constraints for medical image segmentation
by: Gaillochet, Mélanie, et al.
Published: (2025)
by: Gaillochet, Mélanie, et al.
Published: (2025)
Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision
by: Gaillochet, Mélanie, et al.
Published: (2024)
by: Gaillochet, Mélanie, et al.
Published: (2024)
Exploring Entropy-based Active Learning for Fair Brain Segmentation
by: Danaee, Ghazal, et al.
Published: (2026)
by: Danaee, Ghazal, et al.
Published: (2026)
Pitfalls of topology-aware image segmentation
by: Berger, Alexander H., et al.
Published: (2024)
by: Berger, Alexander H., et al.
Published: (2024)
Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation
by: Santini, Francesco, et al.
Published: (2023)
by: Santini, Francesco, et al.
Published: (2023)
ReC-TTT: Contrastive Feature Reconstruction for Test-Time Training
by: Colussi, Marco, et al.
Published: (2024)
by: Colussi, Marco, et al.
Published: (2024)
Revisiting MAE pre-training for 3D medical image segmentation
by: Wald, Tassilo, et al.
Published: (2024)
by: Wald, Tassilo, et al.
Published: (2024)
COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images
by: Shvetsov, Dmytro, et al.
Published: (2024)
by: Shvetsov, Dmytro, et al.
Published: (2024)
Test-time augmentation improves efficiency in conformal prediction
by: Shanmugam, Divya, et al.
Published: (2025)
by: Shanmugam, Divya, et al.
Published: (2025)
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
by: Gu, Hanxue, et al.
Published: (2024)
by: Gu, Hanxue, et al.
Published: (2024)
Uncertainty evaluation of segmentation models for Earth observation
by: Rey, Melanie, et al.
Published: (2025)
by: Rey, Melanie, et al.
Published: (2025)
Next day fire prediction via semantic segmentation
by: Alexis, Konstantinos, et al.
Published: (2024)
by: Alexis, Konstantinos, et al.
Published: (2024)
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
by: Basaran, Berke Doga, et al.
Published: (2024)
by: Basaran, Berke Doga, et al.
Published: (2024)
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset
by: Woerner, Stefano, et al.
Published: (2024)
by: Woerner, Stefano, et al.
Published: (2024)
MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning
by: Nicolas, Julien, et al.
Published: (2023)
by: Nicolas, Julien, et al.
Published: (2023)
Bridging spatial awareness and global context in medical image segmentation
by: Alzu'bi, Dalia, et al.
Published: (2025)
by: Alzu'bi, Dalia, et al.
Published: (2025)
BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks
by: Chen, Gongping, et al.
Published: (2023)
by: Chen, Gongping, et al.
Published: (2023)
Comprehensive language-image pre-training for 3D medical image understanding
by: Wald, Tassilo, et al.
Published: (2025)
by: Wald, Tassilo, et al.
Published: (2025)
Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis
by: Abboud, Zeinab, et al.
Published: (2024)
by: Abboud, Zeinab, et al.
Published: (2024)
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?
by: Zhang, Yixin, et al.
Published: (2024)
by: Zhang, Yixin, et al.
Published: (2024)
Comparison of fine-tuning strategies for transfer learning in medical image classification
by: Davila, Ana, et al.
Published: (2024)
by: Davila, Ana, et al.
Published: (2024)
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification
by: Castro-Macías, Francisco M., et al.
Published: (2024)
by: Castro-Macías, Francisco M., et al.
Published: (2024)
BodyGPS: Anatomical Positioning System
by: Yerebakan, Halid Ziya, et al.
Published: (2025)
by: Yerebakan, Halid Ziya, et al.
Published: (2025)
BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging
by: Sherkatghanad, Zeinab, et al.
Published: (2024)
by: Sherkatghanad, Zeinab, et al.
Published: (2024)
Systematic comparison of semi-supervised and self-supervised learning for medical image classification
by: Huang, Zhe, et al.
Published: (2023)
by: Huang, Zhe, et al.
Published: (2023)
Augmented Equivariant Mesh Networks for Anatomical Segmentation
by: Saragih, Daniel
Published: (2026)
by: Saragih, Daniel
Published: (2026)
SPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
by: Mahatha, Kunal, et al.
Published: (2026)
by: Mahatha, Kunal, et al.
Published: (2026)
SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images
by: Specktor-Fadida, Bella, et al.
Published: (2024)
by: Specktor-Fadida, Bella, et al.
Published: (2024)
Real-time prediction of breast cancer sites using deformation-aware graph neural network
by: Lee, Kyunghyun, et al.
Published: (2025)
by: Lee, Kyunghyun, et al.
Published: (2025)
3D Transport-based Morphometry (3D-TBM) for medical image analysis
by: Kan, Hongyu, et al.
Published: (2026)
by: Kan, Hongyu, et al.
Published: (2026)
Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals
by: André, Pascaline, et al.
Published: (2026)
by: André, Pascaline, et al.
Published: (2026)
Bridging visual saliency and large language models for explainable deep learning in medical imaging
by: Nguezet, Paul Valery, et al.
Published: (2026)
by: Nguezet, Paul Valery, et al.
Published: (2026)
Soft-CAM: Making black box models self-explainable for medical image analysis
by: Djoumessi, Kerol, et al.
Published: (2025)
by: Djoumessi, Kerol, et al.
Published: (2025)
Is the medical image segmentation problem solved? A survey of current developments and future directions
by: Xu, Guoping, et al.
Published: (2025)
by: Xu, Guoping, et al.
Published: (2025)
Enhancing pretraining efficiency for medical image segmentation via transferability metrics
by: Hidy, Gábor, et al.
Published: (2024)
by: Hidy, Gábor, et al.
Published: (2024)
PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction
by: MacRae, Daniel C., et al.
Published: (2026)
by: MacRae, Daniel C., et al.
Published: (2026)
A multimodal slice discovery framework for systematic failure detection and explanation in medical image classification
by: Liu, Yixuan, et al.
Published: (2026)
by: Liu, Yixuan, et al.
Published: (2026)
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
by: Zheng, Guangyao, et al.
Published: (2023)
by: Zheng, Guangyao, et al.
Published: (2023)
CLIPArTT: Adaptation of CLIP to New Domains at Test Time
by: Hakim, Gustavo Adolfo Vargas, et al.
Published: (2024)
by: Hakim, Gustavo Adolfo Vargas, et al.
Published: (2024)
NC-TTT: A Noise Contrastive Approach for Test-Time Training
by: Osowiechi, David, et al.
Published: (2024)
by: Osowiechi, David, et al.
Published: (2024)
Similar Items
-
Prompt learning with bounding box constraints for medical image segmentation
by: Gaillochet, Mélanie, et al.
Published: (2025) -
Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision
by: Gaillochet, Mélanie, et al.
Published: (2024) -
Exploring Entropy-based Active Learning for Fair Brain Segmentation
by: Danaee, Ghazal, et al.
Published: (2026) -
Pitfalls of topology-aware image segmentation
by: Berger, Alexander H., et al.
Published: (2024) -
Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation
by: Santini, Francesco, et al.
Published: (2023)