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The paper introduces Dual Co-Train, a source‑free domain adaptation framework for ultrasound tongue segmentation that operates under extreme data scarcity. Starting from a lightweight UltraUNet pretrained on only five labeled images, the method iteratively refines pseudo‑labels, filters unreliable masks with a contour‑based quality‑control module, and generates target‑style synthetic image‑mask pairs via a segmentation‑guided conditional GAN. The student model is trained on a mix of clean pseudo‑labeled target images, noisy pseudo‑labels with consistency regularization, and synthetic samples, enabling closed‑loop adaptation without access to source data. Experiments on 12 source‑target transfer pairs across eight datasets show that this approach improves segmentation overlap and contour accuracy over both unsupervised baselines and supervised models.
arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.
arXiv:2511.05782v3 Announce Type: replace Abstract: Unsupervised domain adaptation (UDA) for medical image segmentation remains challenging due to substantial domain shifts across imaging modalities,...
The paper introduces ANT, a test‑time adaptation framework that improves prostate cancer detection in micro‑ultrasound by performing a segmentation‑guided adaptation. ANT aligns a pretrained detection encoder to the target domain’s prostate anatomy using pseudo‑masks from a frozen segmentation network, thereby correcting domain‑specific feature drift while preserving cancer‑discriminative features. In a leave‑one‑center‑out evaluation, ANT raises mean AUC by 2.9% at the biopsy‑core level and 3.6% at the patient level compared to no adaptation, outperforming existing TTA baselines.
arXiv:2605. 25402v2 Announce Type: replace-cross Abstract: Self-supervised pre-training paradigm has gained increasing prominence for learning transferable representations in medical imaging, yet existing methods for ultrasound (US) images operate at the image or frame level, overlooking the anatomical context for clinical-aligned representation learning.
The paper introduces an unsupervised domain adaptation framework that aligns redundancy-reducing features to enable accurate 3D segmentation of cone-beam CT (CBCT) without target-domain annotations or inference-time adaptation. The method is architecture-agnostic, working with both CNN-based and ViT-based foundation models, and is evaluated on two liver segmentation benchmarks for interventional vascular procedures and radiation therapy. Results show that even large pretrained segmentation networks need explicit feature-space bridging to generalize across diagnostic CT and CBCT, and the proposed approach consistently outperforms existing pretrained foundation models and UDA strategies.