FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

• URL: http://arxiv.org/abs/2607.18283v1
• Date: Tue, 30 Jun 2026 09:02:13 GMT
• Title: FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images
• Abstract summary: We propose FedCC, a framework for corpus callosum (CC) localization in fetal US images.<n>The framework integrates a frozen DINOv2 backbone with a lightweight YOLO-based detection head.<n>It substantially reduces both computational and communication overhead, making the framework suitable for low-resource environments.
• Score: 34.592227505836156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities. However, this task remains highly challenging due to the intrinsic limitations of US imaging, including low contrast, speckle noise, and the considerable anatomical variability of the CC. We propose FedCC, a federated learning (FL)-based framework for CC localization in fetal US images, specifically designed for realistic multi-center and resource-constrained clinical settings without requiring data sharing. The framework integrates a frozen DINOv2 backbone with a lightweight YOLO-based detection head. To enable parameter-efficient adaptation, Low-Rank Adaptation (LoRA) modules are incorporated, allowing only a small subset of parameters to be optimized and exchanged among clients. This strategy substantially reduces both computational and communication overhead, making the framework suitable for low-resource environments. The proposed approach was evaluated on a multi-center dataset comprising 10,970 ultrasound frames acquired from 58 pregnant women during routine neurosonographic examinations across three clinical sites using heterogeneous imaging devices. The proposed framework achieved strong performance in the federated setting. In particular, the combination of DINOv2 and LoRA under the FedAvg strategy achieved an average mAP@50 of 0.857 and an F1-score of 0.803, outperforming both full fine-tuning and encoder-freezing baselines. Notably, the proposed approach reduced the number of trainable parameters to 2.9M compared with 24.4M in full fine-tuning, corresponding to an approximately 8.5$\times$ reduction in communication cost. These findings represent a promising step toward scalable, privacy-preserving, and clinically deployable AI systems for fetal neurosonography.

Related papers

• Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images [48.646448077284276]
  We propose Spatial-FAD, a framework for effective few-shot medical anomaly detection.<n>It improves lesion localization by combining VFM spatial priors with CLIP semantics.<n>Our method achieves an average improvement of over 11.4% in Dice score and 1.8% in AUC.
  arXiv  Detail & Related papers  (2026-09-11T05:21:01Z)
• SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis [51.81845420301345]
  SonoCLIP is a vision-language foundation model for fetal ultrasound analysis.<n>It integrates segmentation masks as mask-channel visual prompts within the vision encoder.<n>It achieves superior zero-shot transfer performance under both global and mask-guided inference.
  arXiv  Detail & Related papers  (2026-06-28T20:04:49Z)
• Differential Attention-Augmented BiomedCLIP with Asymmetric Focal Optimization for Imbalanced Multi-Label Video Capsule Endoscopy Classification [1.2246649738388389]
  This work presents a multi-label classification framework for video capsule endoscopy (VCE)<n>It addresses the extreme class imbalance inherent in the Galar dataset through a combination of architectural and optimization-level strategies.
  arXiv  Detail & Related papers  (2026-03-18T16:04:50Z)
• A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis [38.31064949197421]
  Generative Adversarial Network (GAN) architectures have been proposed for MRI-to-CT translation.<n>We present a benchmark of ten GAN evaluated on the SynthRAD2025 dataset across three anatomical districts.
  arXiv  Detail & Related papers  (2026-03-13T18:54:20Z)
• Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI [1.2782432265370094]
  We propose Med-DualLoRA, a client-aware parameter-efficient fine-tuning framework that disentangles globally shared and local low-rank adaptations (LoRA) through additive decomposition.<n>We evaluate our method on a multi-center state-of-the-art cine 3D CMR FM fine-tuned for disease detection using ACDC and combined M&Ms datasets.
  arXiv  Detail & Related papers  (2026-03-11T16:52:21Z)
• Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines [43.505945728449774]
  Global token mixing has become a popular model design choice for MRI restoration.<n>We ask whether global token mixing is actually beneficial in each individual task across three representative settings.<n>For accelerated MRI reconstruction, the minimal unrolled gated-CNN baseline is already highly competitive.<n>For super-resolution, where low-frequency k-space data are largely preserved by the controlled low-pass degradation, local gated models remain competitive.<n>For denoising with pronounced spatially heteroscedastic noise, token-mixing models achieve the strongest overall performance.
  arXiv  Detail & Related papers  (2026-03-02T04:57:52Z)
• A WDLoRA-Based Multimodal Generative Framework for Clinically Guided Corneal Confocal Microscopy Image Synthesis in Diabetic Neuropathy [8.701084151107652]
  Corneal Confocal Microscopy is a sensitive tool for assessing small-fiber damage in Diabetic Peripheral Neuropathy (DPN)<n>Development of robust, automated deep learning-based diagnostic models is limited by scarce labelled data and fine-grained variability in corneal nerve morphology.<n>We propose a Weight-Decomposed Low-Rank Adaptation (WDLoRA)-based multimodal generative framework for clinically guided CCM image synthesis.
  arXiv  Detail & Related papers  (2026-02-14T09:32:44Z)
• S2M-Net: Spectral-Spatial Mixing for Medical Image Segmentation with Morphology-Aware Adaptive Loss [0.0]
  Medical image segmentation requires balancing local precision for boundary-critical clinical applications, global context for anatomical coherence, and computational efficiency for deployment on limited data and hardware a trilemma that existing architectures fail to resolve.<n>We propose S2M-Net, a 4.7M- parameter architecture that achieves global context through two synergistic innovations: (i) Spectral-Selective Token Mixer (SSTM), which exploits the spectral concentration of medical images via truncated 2D FFT with learnable frequency filtering and content-gated spatial projection, avoiding quadratic attention cost while maintaining global receptive fields; and (ii) Morphology-Aware Adaptive Loss (MAS
  arXiv  Detail & Related papers  (2026-01-03T21:03:54Z)
• UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation [47.18256805790662]
  The Segment Anything Model 2 (SAM2) demonstrates remarkable universal segmentation capabilities on natural images.<n>This limitation raises two critical challenges: how to efficiently adapt SAM2 to ultrasound imaging while maintaining parameter efficiency, and how to deploy the adapted model effectively in resource-constrained clinical environments.<n>We propose UniUltra for universal ultrasound segmentation. Specifically, we first introduce a novel context-edge hybrid adapter (CH-Adapter) that enhances fine-grained perception across diverse ultrasound imaging modalities while achieving parameter-efficient fine-tuning.
  arXiv  Detail & Related papers  (2025-11-19T17:42:49Z)
• FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis [1.7239326521335454]
  Fed SynthCT-Brain is an approach based on the Federated Learning paradigm for MRI-to-sCT in brain imaging.<n>Cross-silo horizontal FL approach allows multiple centres to collaboratively train a U-Net-based deep learning model.
  arXiv  Detail & Related papers  (2024-12-09T17:32:54Z)
• Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification [42.75911994044675]
  We present a novel approach for unpaired image-to-image translation of prostate MRIs and an uncertainty-aware training approach for classifying clinically significant PCa. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work.
  arXiv  Detail & Related papers  (2023-07-02T05:26:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.