NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submissio

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies.

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future

Federated Learning for Breast Density Classification: A Real-World Implementation

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.Comment: Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig.

Additional file 1 of Dimensional changes of upper airway after slow vs rapid miniscrew-supported maxillary expansion in adolescents: a cone-beam computed tomography study

Additional file 1: Table 1. Reliability analysis using ICC

Does maxillary sinus proximity affect molar root resorption during distalization using Invisalign? a CBCT study

Abstract Background This study aimed to assess the correlation between maxillary sinus proximity to root apices of maxillary molars and root resorption during molar distalization using clear aligner therapy (CAT). Materials and methods Thirty-eight cone beam computed tomography scans (CBCTs) obtained pre- (T0) and post-treatment (T1) from 19 adult patients (36.68 ± 13.50 years), who underwent maxillary molar distalization using Invisalign® aligners (Align Technology, Inc., San José, CA, USA) with a minimum of 2 mm distalization, were evaluated in this study At least 22 h of aligner wear per day was a main inclusion criterion. Sinus proximity and changes in root lengths were measured for 61 molars (183 roots). Spearman coefficient analysis was used for assessing correlation between sinus proximity and root resorption. The level of significance was set at p ≤ 0.05. The reproducibility of measurements was assessed by intraclass correlation coefficient (ICC). Results Spearman coefficient revealed no significant correlation between sinus proximity and molar root resorption for mesiobuccal, distobuccal or palatal roots (p = 0.558, p = 0.334, p = 0.931, respectively). Conclusion There was no correlation between maxillary sinus proximity to root apices of maxillary molars and root resorption

Implementation and prospective real-time evaluation of a generalized system for in-clinic deployment and validation of machine learning models in radiology.

The medical imaging community has embraced Machine Learning (ML) as evidenced by the rapid increase in the number of ML models being developed, but validating and deploying these models in the clinic remains a challenge. The engineering involved in integrating and assessing the efficacy of ML models within the clinical workflow is complex. This paper presents a general-purpose, end-to-end, clinically integrated ML model deployment and validation system implemented at UCSF. Engineering and usability challenges and results from 3 use cases are presented. A generalized validation system based on free, open-source software (OSS) was implemented, connecting clinical imaging modalities, the Picture Archiving and Communication System (PACS), and an ML inference server. ML pipelines were implemented in NVIDIAs Clara Deploy framework with results and clinician feedback stored in a customized XNAT instance, separate from the clinical record but linked from within PACS. Prospective clinical validation studies of 3 ML models were conducted, with data routed from multiple clinical imaging modalities and PACS. Completed validation studies provided expert clinical feedback on model performance and usability, plus system reliability and performance metrics. Clinical validation of ML models entails assessing model performance, impact on clinical infrastructure, robustness, and usability. Study results must be easily accessible to participating clinicians but remain outside the clinical record. Building a system that generalizes and scales across multiple ML models takes the concerted effort of software engineers, clinicians, data scientists, and system administrators, and benefits from the use of modular OSS. The present work provides a template for institutions looking to translate and clinically validate ML models in the clinic, together with required resources and expected challenges

The HCG ratio as a predictor of pregnancy outcome in assisted conception cycles

Objective: To determine whether the HCG ratio can be used to predict pregnancy viability in patients undergoing IVF/ICSI treatment. Design and settings: This was a prospective observational study conducted in a private assisted conception unit. Subjects and methods: The patients recruited had one either a long luteal agonist protocol, a short agonist protocol, or an antagonist protocol. All patients had a maximum of three embryos transferred per cycle. Pregnancy detection was by routine serum HCG measurement on day 14 after oocyte retrieval (HCG 0) followed by another HCG sample 48h later (HCG 48). Patients with an initial positive HCG had a transvaginal ultrasound 14days later to determine viability. Results: Three hundred and twenty patients were included in the study. We used receiver operating characteristics (ROC) analysis to predict the ability of HCG measured at 14days (HCG 0), HCG measured at 16days (HCG 48) after oocyte retrieval as well as the HCG ratio (HCG 48/HCG 0) to predict pregnancy viability as well as to predict multiple pregnancy. The HCG ratio with an optimal cut-off of 1.82 had a sensitivity of 97.6%, a specificity of 98.2% and an area under the ROC curve of 98% in the prediction of pregnancy viability. In the prediction of multiple pregnancy the HCG ratio had an optimal cut-off of 2.06 with a sensitivity of 94.5% and a specificity of only 35.6% and an area under of only the ROC curve of 64%. However, the HCG 0 with a cut-off value of 118.56mIU/ml (sensitivity 97%, specificity 96.5%) and the HCG 48 with a cut-off value of 258.16mIU/ml (sensitivity 97.2%, specificity 99.4%) were shown to be accurate in predicting a viable intrauterine multiple pregnancy with an area under the ROC curve of 97% and 99%, respectively. Conclusion: The HCG ratio with a cut-off value of 1.82 can be used to predict pregnancy viability in assisted conception cycles. Also HCG measured 14 and 16days after oocyte retrieval with a cut-off value of 118.56mIU/ml and 258mIU/ml can be used to predict viable multiple pregnancy