Few-Shot Medical Image Segmentation

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Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
Apr 2, 2026Sebo Diaz, Polina Golland, Elfar Adalsteinsson +1Medical Image Segmentation 3D Medical Image Segmentation
MIT · MIT, MGH, HMS
RAP: Retrieve, Adapt, and Prompt-Fit for Training-Free Few-Shot Medical Image Segmentation
Mar 29, 2026Zhihao Mao, Bangpu ChenMedical Image Segmentation Few-Shot Medical Image Segmentation
School of Computer Science, China University of Geosciences, Wuhan, China
Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
Mar 26, 2026Yusri Al-Sanaani, Rebecca Thornhill, Pablo Nery +5Medical Image Segmentation 3D Medical Image Segmentation
Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada · Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa, Ottawa, Canada · Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, Ottawa, Canada
Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Prompting
Mar 22, 2026Yuntian Bo, Yazhou Zhu, Piotr Koniusz +1Few-Shot Medical Image Segmentation Promptable Image Segmentation
Nanjing University of Science and Technology · University of New South Wales · Data61 CSIRO
Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision
Mar 14, 2026Yunhe Gao, Yabin Zhang, Chong Wang +5Medical Image Representation Learning Self-Supervised Pre-Training
Stanford University
Ensemble Learning with Sparse Hypercolumns
Mar 6, 2026Julia Dietlmeier, Vayangi Ganepola, Oluwabukola G. Adegboro +3Few-Shot Medical Image Segmentation Ensemble Learning
Insight Research Ireland Centre for Data Analytics, Dublin City University, Glasnevin 9, Dublin, Ireland · Research Ireland Centre for Research Training in Machine Learning (ML-Labs), Dublin City University, Glasnevin 9, Dublin, Ireland · School of Computing, Dublin City University, Glasnevin 9, Dublin, Ireland
Shifting Adaptation from Weight Space to Memory Space: A Memory-Augmented Agent for Medical Image Segmentation
Mar 6, 2026Bowen Chen, Qiaohui Gao, Shaowen Wan +5Medical Image Segmentation Few-Shot Medical Image Segmentation
Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106, USA · College of Engineering, Northeastern University, Boston, MA 02115, USA · Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA +4
Self-Auditing Parameter-Efficient Fine-Tuning for Few-Shot 3D Medical Image Segmentation
Mar 6, 2026Son Thai Ly, Hien V. NguyenParameter-Efficient Fine-Tuning Few-Shot Medical Image Segmentation
University of Houston
VesselBridge3D: A Foundation Model Adaptation Framework for Label-Efficient 3D Vessel Segmentation
Feb 27, 2026Kirato Yoshihara, Yohei Sugawara, Yuta Tokuoka +13D Medical Image Segmentation Few-Shot Medical Image Segmentation
The University of Osaka · Preferred Networks, Inc.
BiCLIP: Bidirectional and Consistent Language-Image Processing for Robust Medical Image Segmentation
Feb 25, 2026Saivan Talaei, Fatemeh Daneshfar, Abdulhady Abas Abdullah +1Multimodal Robustness Medical VLMs
Department of Computer Engineering, University of Kurdistan, Sanandaj, Kurdistan, Iran · Artificial Intelligence and Innovation Center, University of Kurdistan, Erbil, Kurdistan, Iraq · Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland

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Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It

Sebo Diaz, Polina Golland, Elfar Adalsteinsson, Neel Dey

MIT · MIT, MGH, HMS

Medical Image Segmentation 3D Medical Image Segmentation Few-Shot Medical Image Segmentation

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We present MaskGen, a theoretically grounded and deliberately simple approach for domain generalization in 3D biomedical image segmentation. Modern segmentation models degrade sharply under shifts in modality, disease severity, clinical sites, and more, limiting their reliable adoption. Existing generalization methods address this using extreme augmentations, hand-engineered domain statistics mixing, or architectural redesigns that add significant implementation overhead while yielding inconsistent performance across biomedical settings. MaskGen instead presents a principled learning strategy with marginal overhead that utilizes both source-domain image intensities and domain-stable foundation model representations to train robust segmentation models. As a result, MaskGen achieves strong gains in both fully supervised and few-shot segmentation across broad clinical shifts in biomedical studies. Unlike prior approaches, MaskGen is architecture- and loss-agnostic, compatible with standard augmentation pipelines, easy to implement, and tackles arbitrary anatomical regions. Its implementation is freely available at https://github.com/sebodiaz/MaskGen.