1,610 research outputs found
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Preservation of Patient Level Privacy: Federated Classification and Calibration Models
With the launching of the Precision Medicine Initiative in the United States, by the National Institute of Health, and the emergence of a large volume of electronic health records, there are many opportunities to improve clinical decision support systems. A large number of samples are needed to build predictive models that have adequate discrimination and calibration. However, protecting patient privacy is also an important issue. Patient data are typically protected in localized silos, and consolidation of datasets from different healthcare systems is difficult. Federated learning allows the training of a global model by amassing intermediate calculations from localized medical systems. The knowledge learned from the data can be transferred and aggregated to achieve better performance than the one achieved by individual local models. Federated learning may help build better models, providing more accurate predictions. There are two types of measures to assess how well a model performs: discrimination and calibration. While most papers report discrimination measures, calibration has often been neglected but it is a critical metric for evaluation. In this dissertation, I show a novel way to build classifiers and calibration models in a federated manner. I also show how I can evaluate and improve model calibration in this manner. Federated modeling enables the accumulation of knowledge and information that are otherwise locked behind local medical systems
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Toward fairness in artificial intelligence for medical image analysis: Identification and mitigation of potential biases in the roadmap from data collection to model deployment
Purpose: To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups. Approach: Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development. Results: Five main steps along the roadmap of medical imaging AI/ML were identified: (1) data collection, (2) data preparation and annotation, (3) model development, (4) model evaluation, and (5) model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies. Conclusions: Our findings provide a valuable resource to researchers, clinicians, and the public at large.</p
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms
We propose a novel energy-aware federated learning (FL)-based system, namely
SusFL, for sustainable smart farming to address the challenge of inconsistent
health monitoring due to fluctuating energy levels of solar sensors. This
system equips animals, such as cattle, with solar sensors with computational
capabilities, including Raspberry Pis, to train a local deep-learning model on
health data. These sensors periodically update Long Range (LoRa) gateways,
forming a wireless sensor network (WSN) to detect diseases like mastitis. Our
proposed SusFL system incorporates mechanism design, a game theory concept, for
intelligent client selection to optimize monitoring quality while minimizing
energy use. This strategy ensures the system's sustainability and resilience
against adversarial attacks, including data poisoning and privacy threats, that
could disrupt FL operations. Through extensive comparative analysis using
real-time datasets, we demonstrate that our FL-based monitoring system
significantly outperforms existing methods in prediction accuracy, operational
efficiency, system reliability (i.e., mean time between failures or MTBF), and
social welfare maximization by the mechanism designer. Our findings validate
the superiority of our system for effective and sustainable animal health
monitoring in smart farms. The experimental results show that SusFL
significantly improves system performance, including a reduction in
energy consumption, a increase in social welfare, and a rise in
Mean Time Between Failures (MTBF), alongside a marginal increase in the global
model's prediction accuracy
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
Machine learning is expected to fuel significant improvements in medical
care. To ensure that fundamental principles such as beneficence, respect for
human autonomy, prevention of harm, justice, privacy, and transparency are
respected, medical machine learning systems must be developed responsibly. Many
high-level declarations of ethical principles have been put forth for this
purpose, but there is a severe lack of technical guidelines explicating the
practical consequences for medical machine learning. Similarly, there is
currently considerable uncertainty regarding the exact regulatory requirements
placed upon medical machine learning systems. This survey provides an overview
of the technical and procedural challenges involved in creating medical machine
learning systems responsibly and in conformity with existing regulations, as
well as possible solutions to address these challenges. First, a brief review
of existing regulations affecting medical machine learning is provided, showing
that properties such as safety, robustness, reliability, privacy, security,
transparency, explainability, and nondiscrimination are all demanded already by
existing law and regulations - albeit, in many cases, to an uncertain degree.
Next, the key technical obstacles to achieving these desirable properties are
discussed, as well as important techniques to overcome these obstacles in the
medical context. We notice that distribution shift, spurious correlations,
model underspecification, uncertainty quantification, and data scarcity
represent severe challenges in the medical context. Promising solution
approaches include the use of large and representative datasets and federated
learning as a means to that end, the careful exploitation of domain knowledge,
the use of inherently transparent models, comprehensive out-of-distribution
model testing and verification, as well as algorithmic impact assessments
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Despite significant improvements over the last few years, cloud-based
healthcare applications continue to suffer from poor adoption due to their
limitations in meeting stringent security, privacy, and quality of service
requirements (such as low latency). The edge computing trend, along with
techniques for distributed machine learning such as federated learning, have
gained popularity as a viable solution in such settings. In this paper, we
leverage the capabilities of edge computing in medicine by analyzing and
evaluating the potential of intelligent processing of clinical visual data at
the edge allowing the remote healthcare centers, lacking advanced diagnostic
facilities, to benefit from the multi-modal data securely. To this aim, we
utilize the emerging concept of clustered federated learning (CFL) for an
automatic diagnosis of COVID-19. Such an automated system can help reduce the
burden on healthcare systems across the world that has been under a lot of
stress since the COVID-19 pandemic emerged in late 2019. We evaluate the
performance of the proposed framework under different experimental setups on
two benchmark datasets. Promising results are obtained on both datasets
resulting in comparable results against the central baseline where the
specialized models (i.e., each on a specific type of COVID-19 imagery) are
trained with central data, and improvements of 16\% and 11\% in overall
F1-Scores have been achieved over the multi-modal model trained in the
conventional Federated Learning setup on X-ray and Ultrasound datasets,
respectively. We also discuss in detail the associated challenges,
technologies, tools, and techniques available for deploying ML at the edge in
such privacy and delay-sensitive applications.Comment: preprint versio
Federated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing
Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling
Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the wrong course of action. In this article, a novel approach called federated data quality profiling (FDQP) is proposed to assess the quality of the data at the edge. FDQP is inspired by federated learning (FL) and serves as a condensed document or a guide for node data quality assurance. The FDQP formal model is developed to capture the quality dimensions specified in the data quality profile (DQP). The proposed approach uses federated feature selection to improve classifier precision and rank features based on criteria such as feature value, outlier percentage, and missing data percentage. Extensive experimentation using a fetal dataset split into different edge nodes and a set of scenarios were carefully chosen to evaluate the proposed FDQP model. The results of the experiments demonstrated that the proposed FDQP approach positively improved the DQ, and thus, impacted the accuracy of the federated patient similarity network (FPSN)-based machine learning models. The proposed data-quality-aware federated PSN architecture leveraging FDQP model with data collected from edge nodes can effectively improve the data quality and accuracy of the federated patient similarity network (FPSN)-based machine learning models. Our profiling algorithm used lightweight profile exchange instead of full data processing at the edge, which resulted in optimal data quality achievement, thus improving efficiency. Overall, FDQP is an effective method for assessing data quality in the edge computing environment, and we believe that the proposed approach can be applied to other scenarios beyond patient monitoring
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