ADAPT is a new pre-training paradigm that aligns physical properties of time-series data to allow simultaneous training on 162 diverse classification datasets, achieving new state-of-the-art performance.
The limits of fair medical imaging AI in real -world generalization
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Family-FL uses family-level aggregation in a three-tier setup with a sub-5KB quantized CNN-LSTM to cut communication by 76.7% versus FedAvg while reaching 91.9% accuracy on MIT-BIH arrhythmia data.
MicroDiffuse3D is a foundation model that restores 3D microscopy images under sparse super-resolution, joint degradation, and low-SNR denoising, reporting 10.58% segmentation and 15.59% line-profile gains over baselines.
Domain-adapted ECG foundation models with self-supervised pretraining and selective fine-tuning reach macro-AUROC 0.8509 for multi-label structural heart disease detection on the EchoNext benchmark.
Nine care-trajectory clusters derived from Dynamic Time Warping and hierarchical clustering independently predict mortality in cancer patients and show an inverse link to baseline anxiety in high-utilization groups.
Foundation models excel at pattern recognition in biomedical imaging but lack causal reasoning, robustness, and safety for real-world use, so they should augment rather than replace clinical expertise according to the proposed REAL-FM assessment framework.
Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.
This review summarizes how large language models are being used for workflow automation, clinical decision support, and patient engagement in radiation oncology.
citing papers explorer
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ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classification
ADAPT is a new pre-training paradigm that aligns physical properties of time-series data to allow simultaneous training on 162 diverse classification datasets, achieving new state-of-the-art performance.
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Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
Family-FL uses family-level aggregation in a three-tier setup with a sub-5KB quantized CNN-LSTM to cut communication by 76.7% versus FedAvg while reaching 91.9% accuracy on MIT-BIH arrhythmia data.
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MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration
MicroDiffuse3D is a foundation model that restores 3D microscopy images under sparse super-resolution, joint degradation, and low-SNR denoising, reporting 10.58% segmentation and 15.59% line-profile gains over baselines.
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Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
Domain-adapted ECG foundation models with self-supervised pretraining and selective fine-tuning reach macro-AUROC 0.8509 for multi-label structural heart disease detection on the EchoNext benchmark.
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Care Trajectories Are Linked to Mental Health and Mortality in Cancer Patients
Nine care-trajectory clusters derived from Dynamic Time Warping and hierarchical clustering independently predict mortality in cancer patients and show an inverse link to baseline anxiety in high-utilization groups.
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Foundation Models in Biomedical Imaging: Turning Hype into Reality
Foundation models excel at pattern recognition in biomedical imaging but lack causal reasoning, robustness, and safety for real-world use, so they should augment rather than replace clinical expertise according to the proposed REAL-FM assessment framework.
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Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows
Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.
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Applications of Large Language Models in Radiation Oncology: From Workflow Automation to Clinical Intelligence
This review summarizes how large language models are being used for workflow automation, clinical decision support, and patient engagement in radiation oncology.