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NIHCC

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The dataset in question is specifically designed to provide insights into [specific topic or focus of the dataset], offering comprehensive information on [key features or variables included]. It contains detailed data points such as [list relevant parameters or attributes], which are essential for understanding [purpose or application of the dataset]. The primary goal of this dataset is to [state its intended use, e.g., support research, inform decision-making, etc.]. By providing structured and organized information, it serves as a valuable resource for [target audience or field of application].

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相关论文

12

Selective knowledge sharing for privacy-preserving federated distillation without a good teacher

Jiawei ShaoFangzhao WuJun Zhang
Nature Communications
2024
2024/1/8
Vol.15 No.1 p.1-11
While federated learning (FL) is promising for efficient collaborative learning without revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high communication overhead, and struggles to adapt to heterogeneous models. Federated distillation (FD) emerges as an altern...
Computer scienceElectrical and electronic engineeringTechnology
10.1038/S41467-023-44383-9
ISSN:2041-1723

A medical multimodal large language model for future pandemics

Fenglin LiuTingting ZhuXian WuBang YangChenyu You13
Npj Digital Medicine
2023
2023/12/2
Vol.6 No.1 p.1-15
Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available label...
Biomedical engineeringHealth care
10.1038/S41746-023-00952-2
ISSN:2398-6352

A multimodal vision–language model for generalizable annotation-free pathology localization

Hao YangHong-Yu ZhouJiarun LiuWeijian HuangCheng Li15
Nature Biomedical Engineering
2026
2026/1/6
00 p.1-15
Existing deep learning models for defining pathology from clinical imaging data rely on expert annotations and lack generalization capabilities in open clinical environments. Here we present a generalizable vision–language model for Annotation-Free pathology Localization (AFLoc). The core strength o...
Biomedical engineeringMedical research
10.1038/S41551-025-01574-7
ISSN:2157-846X

AI for radiographic COVID-19 detection selects shortcuts over signal

Alex J. DeGraveJoseph D. JanizekSu-In Lee
Nature Machine Intelligence
2021
2021/5/31
00 p.1-10
Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning sy...
Computational sciencePredictive medicineRadiographySARS-CoV-2
10.1038/S42256-021-00338-7
ISSN:2522-5839

Detecting shortcut learning for fair medical AI using shortcut testing

Alexander BrownNenad TomasevJan FreybergYuan LiuAlan Karthikesalingam6
Nature Communications
2023
2023/7/18
Vol.14 No.1 p.1-10
Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is to characterize the (un)fairness of ML models—their tendency to perform differently across subgroups of the population—...
Computer scienceMachine learningMedical imaging
10.1038/S41467-023-39902-7
ISSN:2041-1723

Bridging the interpretability gap for medical artificial intelligence models using class-association manifold learning

Ruitao XieXiaoxi HeLimai JiangMini Han WangJingbang Chen11
Nature Biomedical Engineering
2026
2026/5/18
00 p.1-18
Explainability has increasingly become a core requirement for intelligent medical devices. Current medical artificial intelligence (AI) technologies suffer from the ‘interpretability gap’ despite tremendous efforts for enhancing explainability. Here we propose class-association manifold learning, a ...
Computational scienceMachine learningMedical imagingSoftware
10.1038/S41551-026-01676-W
ISSN:2157-846X

Automated abnormality classification of chest radiographs using deep convolutional neural networks

Yu-Xing TangYou-Bao TangYifan PengKe YanMohammadhadi Bagheri11
Npj Digital Medicine
2020
2020/5/14
Vol.3 No.1 p.1-8
As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workfl...
Biomedical engineeringRadiography
10.1038/S41746-020-0273-Z
ISSN:2398-6352

Exploring scalable medical image encoders beyond text supervision

Fernando Pérez-GarcíaHarshita SharmaSam Bond-TaylorKenza BouzidValentina Salvatelli15
Nature Machine Intelligence
2025
2025/1/13
00 p.1-12
Language-supervised pretraining has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal systems within the computer vision and medical imaging domains. However, the computed features are limited by the informatio...
DiagnosisMedical researchRadiography
10.1038/S42256-024-00965-W
ISSN:2522-5839

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

Farah E. ShamoutYiqiu ShenNan WuAakash KakuJungkyu Park20
Npj Digital Medicine
2021
2021/5/12
Vol.4 No.1 p.1-11
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray ...
Biomedical engineeringComputational scienceComputer scienceRadiography
10.1038/S41746-021-00453-0
ISSN:2398-6352

The limits of fair medical imaging AI in real-world generalization

Yuzhe YangHaoran ZhangJudy W. GichoyaDina KatabiMarzyeh Ghassemi
Nature Medicine
2024
2024/6/28
00 p.1-11
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research established AI’s capacity to infer demographic data from chest X-rays, leading to a key concern: do models u...
DiagnosisRadiography
10.1038/S41591-024-03113-4
ISSN:1078-8956

Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization

Tianyu HanSven NebelungFederico PedersoliMarkus ZimmermannMaximilian Schulze-Hagen11
Nature Communications
2021
2021/7/14
Vol.12 No.1 p.1-11
Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterp...
Machine learningMedical imagingPredictive medicine
10.1038/S41467-021-24464-3
ISSN:2041-1723

A multimodal multidomain multilingual medical foundation model for zero shot clinical diagnosis

Fenglin LiuZheng LiQingyu YinJinfa HuangJiebo Luo12
Npj Digital Medicine
2025
2025/2/6
Vol.8 No.1 p.1-12
Radiology images are one of the most commonly used in daily clinical diagnosis. Typically, clinical diagnosis using radiology images involves disease reporting and classification, where the former is a multimodal task whereby textual reports are generated to describe clinical findings in images, as ...
Biomedical engineeringHealth care
10.1038/S41746-024-01339-7
ISSN:2398-6352