Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhil...
The paper evaluates federated learning with Low‑Rank Adaptation (LoRA) for fine‑tuning the BiomedCLIP vision‑language model on chest X‑ray classification across four international cohorts. Federated LoRA improves shared‑class AUC from 0.687 to 0.802, outperforming isolated single‑cohort training and approaching a centralized reference. The study shows that SVD‑based product‑space aggregation (FlexLoRA) is crucial for performance, while FedProx offers no advantage over FedAvg in this setting.
By Sanjaya Poudel, Nirajan Kunwor, Manish Dhakal, Debesh Jha, Sunil Kumar Gaire
arXiv:2509. 10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.
By Rodrigo Tertulino
arXiv:2607. 20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data.
By Afsaneh Mahanipour, Hana Khamfroush
GAMF-Net is a grouped availability‑aware multimodal fusion network designed for smartphone skin lesion classification. It divides ten clinical variables into three semantic groups—demographics, history‑related risk, and lesion presentation—and encodes each group with field values and availability masks. The image branch supplies base logits while each clinical group contributes image‑conditioned residual evidence; only available groups are fused with equal weight, and the model defaults to image‑only when no clinical data are present. Evaluated on PAD‑UFES‑20, GAMF-Net achieves an accuracy of 74.17 % and outperforms image‑only and other multimodal baselines, with ablation studies highlighting the importance of clinical information and constrained equal‑weight fusion.
By Yue Zhuo, Jingjia Yan, Fangyu Liu, Lingjun Liu, Zongjun Chen, Zhisheng Luo, Zhonghua Xie
FedDRAW introduces a new server‑side aggregation technique for federated learning called Federated Dual Reputation Annealing Weighting. It blends a data‑size prior with cosine similarity between client and global model parameters, using two annealing schedules to shift weight from size to similarity and to gradually relax selectivity. Experiments on 12 simulated client partitions of CheXpert and ChestMNIST datasets show FedDRAW outperforms seven federated baselines in AUC and geometric mean of sensitivity and specificity, with statistically significant results.
By Maryam Moradpour, Anne-Christin Hauschild