arXiv AI September 10, 2026 By Chayun Kongtongvattana

Novel Methods for Catheter and Guidewire Segmentation in X-ray Fluoroscopy under a Federated Learning Setting

The paper introduces a structure‑aware federated learning framework for segmenting catheters and guidewires in X‑ray fluoroscopy. It presents a new benchmark dataset, CathAction, and a shape‑sensitive loss that improves segmentation accuracy. The approach extends to federated learning, adding projected gradient descent for adversarial optimization, and includes a diffusion‑based synthetic data generator that boosts performance under data scarcity.

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arXiv AI

Jul 28

Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registration

arXiv:2607. 23343v1 Announce Type: cross Abstract: Intraoperative 2D/3D registration aligns preoperative CT volumes with intraoperative X-ray or fluoroscopic images and is essential for image-guided interventions.

By Minheng Chen, Youyong Kong

computer-vision

arXiv Computer Vision

Sep 10

Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization

arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...

By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot

computer-vision multimodal

arXiv Computer Vision

Aug 28

DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT

DALE-CT introduces depth‑aware 2D slice encoders that learn an anatomical world model of chest CT scans without 3D or positional supervision. By sampling self‑supervised views across a physical $z$‑axis slab, the encoder captures how anatomy changes between neighboring slices, enabling it to recover slice ordering and distinguish slices by anatomy alone. The model, trained on a large 287k‑scan corpus, achieves state‑of‑the‑art performance on CT‑RATE and is released with full code and evaluation tools.

By Evan W. Damron, Mahmut S. Gokmen, Mitchell A. Klusty, Caroline N. Leach, Emily B. Collier, V. K. Cody Bumgardner

llms

arXiv Machine Learning

Aug 7

Benchmark Evaluation of Federated Learning on Multi-organ Images

arXiv:2607. 08219v2 Announce Type: replace-cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.

By Junbin Mao, Xu Tian, Jianchun Zhu, Ludi Li, Jin Liu

benchmarks safety

arXiv AI

Jun 16

Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation

arXiv:2606. 15611v1 Announce Type: cross Abstract: Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology.

By Fuyou Mao, Beining Wu, Yanfeng Jiang, Bohan Xu, Lixin Lin, Naye Ji, Hao Zhang, Yan Tang

computer-vision efficiency benchmarks

arXiv Machine Learning

Jul 10

Benchmark Evaluation of Feredated Learning on Multi-organ Images

arXiv:2607. 08219v1 Announce Type: cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.