Cover Image

Efficient Self-Supervised Adaptation for Medical Image Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sorkhei, Moein, Konuk, Emir, Guo, Jingyu, Meng, Chanjuan, Matsoukas, Christos, Smith, Kevin
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

APLA: A Simple Adaptation Method for Vision Transformers
by: Sorkhei, Moein, et al.
Published: (2025)

Learning from Offline Foundation Features with Tensor Augmentations
by: Konuk, Emir, et al.
Published: (2024)

k-NN as a Simple and Effective Estimator of Transferability
by: Sorkhei, Moein, et al.
Published: (2025)

Pretrained ViTs Yield Versatile Representations For Medical Images
by: Matsoukas, Christos, et al.
Published: (2023)

Learning What Helps: Task-Aligned Context Selection for Vision Tasks
by: Guo, Jingyu, et al.
Published: (2025)

Random Token Fusion for Multi-View Medical Diagnosis
by: Guo, Jingyu, et al.
Published: (2024)

CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
by: Sorkhei, Moein, et al.
Published: (2021)

Self-Supervised Alignment Learning for Medical Image Segmentation
by: Li, Haofeng, et al.
Published: (2024)

PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation
by: Dong, Yue-Jiang, et al.
Published: (2023)

Self-Supervised Contrastive Embedding Adaptation for Endoscopic Image Matching
by: Rota, Alberto, et al.
Published: (2025)

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
by: Meng, Junying, et al.
Published: (2025)

Attention-Based Chaotic Self-Supervision for Medical Image Classification
by: Florindo, Joao Batista, et al.
Published: (2026)

Mask What Matters: Controllable Text-Guided Masking for Self-Supervised Medical Image Analysis
by: Wang, Ruilang, et al.
Published: (2025)

Dynamic and Compressive Adaptation of Transformers From Images to Videos
by: Zhang, Guozhen, et al.
Published: (2024)

Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
by: Zhang, Tianwei, et al.
Published: (2024)

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
by: Pan, Tan, et al.
Published: (2026)

SRA-Seg: Synthetic to Real Alignment for Semi-Supervised Medical Image Segmentation
by: Aranya, OFM Riaz Rahman, et al.
Published: (2026)

MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
by: Zhuang, Jiaxin, et al.
Published: (2024)

Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation
by: Su, Junming, et al.
Published: (2024)

Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective
by: Shen, Zhiqiang, et al.
Published: (2024)

Rotation-Equivariant Self-Supervised Method in Image Denoising
by: Liu, Hanze, et al.
Published: (2025)

Breast Cancer Detection in Thermographic Images via Diffusion-Based Augmentation and Nonlinear Feature Fusion
by: Salem, Sepehr, et al.
Published: (2025)

Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution
by: Chai, Xinning, et al.
Published: (2025)

Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification
by: Singh, Pranav, et al.
Published: (2023)

Semi-Supervised Multi-Modal Medical Image Segmentation for Complex Situations
by: Meng, Dongdong, et al.
Published: (2025)

AMLP: Adjustable Masking Lesion Patches for Self-Supervised Medical Image Segmentation
by: Wang, Xiangtao, et al.
Published: (2023)

VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings
by: Scabini, Leonardo, et al.
Published: (2025)

Bayesian Self-Training for Semi-Supervised 3D Segmentation
by: Unal, Ozan, et al.
Published: (2024)

BCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentation
by: Song, Bentao, et al.
Published: (2026)

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation
by: Zhu, Hongjie, et al.
Published: (2025)

Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework
by: Ma, Mosong, et al.
Published: (2025)

Blackbox Adaptation for Medical Image Segmentation
by: Paranjape, Jay N., et al.
Published: (2024)

Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation
by: Xie, Bin, et al.
Published: (2025)

Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation
by: Liu, Jianfei, et al.
Published: (2024)

Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images
by: Manna, Siladittya, et al.
Published: (2024)

Unified and Semantically Grounded Domain Adaptation for Medical Image Segmentation
by: Wang, Xin, et al.
Published: (2025)

HyDA: Hypernetworks for Test Time Domain Adaptation in Medical Imaging Analysis
by: Serebro, Doron, et al.
Published: (2025)

Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
by: Hu, Jiesi, et al.
Published: (2025)

Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
by: Zhu, Lei, et al.
Published: (2025)

Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging
by: Zhao, Zehui, et al.
Published: (2024)