arXiv AI
Jun 10
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
arXiv:2606. 10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance.
One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification
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.
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arXiv AI
Sep 3
Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts
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.
arXiv AI
Jul 1
Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines
arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.
arXiv Computer Vision
Aug 21
MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities
arXiv:2608. 19788v1 Announce Type: cross Abstract: Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centralized data sharing.
arXiv AI
Sep 11
OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
OmniMed‑FL is a multimodal federated learning framework that fuses chest radiographs and synthetic patient notes to classify five clinical conditions. The study benchmarks eight fusion strategies, three initializations, and four missing‑text imputation rules across 3–20 hospital clients under non‑IID Dirichlet partitioning, showing that federated approaches (FedAvg, FedProx, SCAFFOLD‑AdamW) outperform local‑only training. Multimodal fusion consistently improves performance, achieving macro‑F1 scores up to 0.956 on the synthetic corpus and 0.906 on the radiograph corpus.
arXiv AI
Jun 8
REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference
arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.
arXiv Computer Vision
Aug 25
Exploring learning environments for label\-efficient cancer diagnosis
arXiv:2408.07988v3 Announce Type: replace Abstract: Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial fo...
arXiv Machine Learning
Aug 5
FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare
arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
arXiv Machine Learning
Jul 8
Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography
arXiv:2607. 05628v1 Announce Type: cross Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment.
Hugging Face Trending Papers
Sep 9
OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
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...
arXiv Computer Vision
Sep 22
FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation
arXiv:2609.24627v1 Announce Type: new Abstract: Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and...
arXiv Computer Vision
Sep 22
CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
The CXR‑LT 2026 Challenge introduces a multi‑center, long‑tailed chest X‑ray classification benchmark with over 145,000 radiologist‑annotated images from PadChest and NIH datasets. It defines two core tasks: robust multi‑label classification on 30 known classes and open‑world generalization to 6 unseen rare disease classes. The paper outlines data collection, annotation, solution strategies, and evaluates performance across head‑vs‑tail, calibration, and cross‑center gaps, noting that vision‑language models improve in‑distribution and zero‑shot performance but rare‑finding detection under multi‑center shift remains difficult.