Release Notes

Welcome to the release notes page for the Rhino Health Federated Computing Platform (FCP). This page provides you with a detailed overview of the latest updates and improvements to our platform.

At Rhino Health, we're committed to delivering the best possible experience to our users. We constantly strive to improve our platform by implementing new features, fixing bugs, and enhancing existing functionalities.

Our release notes page is designed to keep you informed about the changes we make to the Rhino Health FCP. Here, you can find information on all the latest updates, including new features, bug fixes, and improvements to the user interface.

Each release note includes a brief summary of the changes we've made, along with any important details you need to know. We will also provide links to additional resources, such as user guides and tutorials, to help you get the most out of the platform, wherever possible.

Our release notes page is an essential resource for staying up-to-date with the latest developments on the Rhino Health FCP. We encourage you to check back regularly for the latest updates, and we welcome your feedback on any aspect of our platform.

2024 Release Notes

The following contains the release notes for 2024, in descending date order.

October 2024

Harmonization Copilot Semantic Mapping enhancements

Quality of life improvements to enhance Reviewing and Editing a Semantic Mapping, export semantic mappings, and provide deployment guidance, including:

  • Added support for searching for values to override recommendations from the system. The search results are constrained to custom vocabulary target terms or OMOP domain terms to expedite finding the right target term.
  • Added a bulk action to set the target value of several entries.
  • Added more information about a Semantic Mapping in a detailed tooltip.
  • When exporting a Semantic Mapping, include the top recommended term in the export for mappings in review.
  • Added hardware benchmarks/recommendations for Rhino Clients for Harmonization Copilot usage.

Harmonization Copilot Syntactic Mapping preliminary release

We are adding exciting new functionality that will allow creating syntactic mappings and transforming data to OMOP v5.4 with a low-code experience. As a step towards that goal, we have released an initial low-code data transformation that can be configured with a JSON file. We are working in maturing this functionality and building the GUI. If you are interested to learn more, feel free to contact us directly

EU based orchestration layer

We have deployed an orchestration layer (aka Rhino Cloud) in the EU so customers that have EU data residency requirements can keep all operations with Rhino within EU. If you would like to learn more how to set up your projects in this new EU based infrastructure, please contact us.

Additional options for Client Mounted Storage

FCP now supports a few additional options for Client-Mounted Storage:

  • S3 access within the VPC without specific IAM credentials
  • Region-specific S3 bucket access

For further information, visit the following documentation article: Client Mounted Storage: How to access data in AWS S3, GCP CS or the SMB network files?

Added metadata when exporting datasets

When working with Datasets in GC code (especially with multi-dataset GC, and even more so with the new “Any” input schema capability) you sometimes want to know some more information about the Datasets in order to decide how to handle them in your code. For example: you may want to handle Datasets from different schemas or workgroups differently, or if you have multiple input Datasets, you might want to handle them in chronological order based on creation time.
In order to make this simpler, we’ve added a new metadata.json file that is exported alongside the dataset.csv and any file_data and/or dicom_data in the Dataset. This metadata file contains the following information:

dataset_uid: str
dataset_name: str
dataset_version: int
data_schema_uid: str
data_schema_name: str
workgroup_uid: str
workgroup_name: str
creation_time: str
num_rows: int
num_fields: int
fields: List[str]

The metadata.json file is created any time a Dataset is exported - manually to /rhino_data, within GC, and in NVFlare. See example below of metadata.json 

{
  "dataset_uid": "a186342e-7a0d-46cd-819d-b751f361108d",
  "dataset_name": "Clinic Dataset",
  "dataset_version": 0,
  "data_schema_uid": "51ee196b-f978-4ca9-9583-6c0309bc3a4a",
  "data_schema_name": "Diabetes Schema",
  "workgroup_uid": "5a2eee3b-dbf2-492d-93fa-1175ae2ad7a9",
  "workgroup_name": "Clinic",
  "creation_time": "2022-09-28 11:51:57.070000+00:00",
  "num_rows": 275,
  "num_fields": 10,
  "fields": [
    "Pregnancies",
    "Age",
    "Height",
    "Weight",
    "Glucose",
    "BloodPressure",
    "SkinThickness",
    "Insulin",
    "DiabetesPedigreeFunction",
    "Outcome"
  ]
}

September 2024

Improvements with Interactive Containers

We have done some major upgrading behind the scenes of Interactive Containers to increase stability with interactive sessions, support TCP connection for compliance with enterprise network security, and improve the user experience.

The user experience improvements include:

  • Added a "initializing" status while the container is being initialized, preventing users from prematurely opening the interactive session and ending in an error state
  • Fixed an intermittent issue where "Import + Terminate" would not successfully import output datasets prior terminating the interactive session

Added option to create Code Objects without having to determine the schema

The Rhino FCP now enables users to create Code Objects without specifying an input Data Schema. This allows creation of Code Objects that can operate more generically across different types of Datasets, improving code reuse and reducing the number of Code Objects needed on FCP. 

See more details in the documentation.

Updated GUI Design

We have been updating the GUI to improve the user experience and match the company brand. We like the new colors' pop, we hope that you do too! :) 

August 2024

Harmonization Copilot for Semantic Mapping

We are excited to introduce a preliminary release of the Harmonization Copilot, a Gen-AI Federated Application for Data Harmonization. In this release, Rhino users can harmonize their data to an OMOP v5.4 Domain, or to a user-defined Custom Vocabulary. The Copilot automatically generates top recommendations for semantic mapping of clinical terms based on natural language, and efficiently allows revision by Clinical Experts. Check it out directly on your FCP account. We are looking forward to hearing your feedback!

For further information, please visit the documentation: About the Harmonization Copilot.

Integration with Microsoft SQL Server

We have added support to directly import data from Microsoft SQL Server via SQL using the Rhino SDK. For further details, please visit Using SQL to Extract Metrics and Import Data From a Database.

July 2024

New Interactive Containers Support for LibreOffice and RStudio

In addition to the existing set of applications available via Rhino FCP such as Jupyter Notebook, 3D Slicer, or QuPath, we have added support to run Interactive Containers with LibreOffice and RStudio.

Users can use these tools directly on FCP to analyze and develop code with real world data within the security and privacy protecting perimeter that FCP provides. The user just needs to create and run an Interactive Container with a container image that has those software packages installed. Check out the User-resources Repo for an example container for LibreOffice. For RStudio, please contact us for support.

See more info in Pushing Containers to the ECR to make your Containers available to FCP, and in What is an Interactive Container Code Object? to learn how to use Interactive Containers. 

LLM Fine-tuning Example

We have added an example of federated fine-tuning of an LLM trained for drug discovery. The example shows how to fine-tune with NVIDIA FLARE 2.4 and BioNeMo an ESM-style model on FCP. See more information in the User Resources Repo.

Improved Default for Output Datasets

To reduce the number of inadvertently generated datasets, we have changed the default output dataset naming template from "{{ input_dataset.0 }}-out" to "{{ input_dataset.0 }}". This will result in creating a new version of the input dataset unless the user explicitly requests a different name for the output dataset. In case of a mistake, the user can always refer to the previous version. This will help with dataset annotation, and also in other use cases. Let us know what you think. See more about output dataset naming templates in Running a Code Object.

Additional Configuration Formats for NVFlare 2.4 (YAML, HOCON)

In addition to JSON, we have added support in FCP to provide NVFlare federated server and federated client configuration in YAML and HOCON. These 2 additional formats are more readable, and supporting them will facilitate running NVFlare 2.4 community examples on FCP. Please find more information about these config formats in NVFlare 2.4 Documentation

Password Reset

FCP now supports password reset for those users using username and password for authentication. Alternatively, users can still log in via SSO. For additional information please refer to the user documentation.

June 2024

Multi-dataset Computing in UI

Users can now create and run Code Objects with multiple input and/or output datasets directly in the UI. This allows users to perform useful data science tasks directly in the UI, such as splitting a dataset in train/test (see example in screenshot below) or combining datasets from different sites. We hope that this feature combined with python auto-containers will bring great quality of life improvements to Rhino users.

See the documentation for more details about creating a Code Object and running a Code Object with multiple datasets. Note that this feature is still supported via the SDK, see an example in the SDK documentation.

Providing Secrets at Run-time

When running Code Objects from the UI, users can now enter run secrets, such as encryption keys or credentials. This allows users to securely provide those secrets without having to recreate the Code Object, saving on disk storage and increasing code re-usability.  This feature is supported for all Code Object types, including NVFlare. It’s now also possible to provide run-time secrets to NVFlare training runs in the UI. For security, the secrets are made available to the container only during run time, and are never persisted anywhere. See more detail in the documentation.

Notable Improvements

  • Improved design and differential privacy setting dropdown when creating a project (see screenshot below)
  • When running NVFlare Code Objects, the user will now be alerted with a warning if the minimum number of clients is smaller than the total number of clients (see screenshot below). If set unintentionally, this setting could result in inconsistent training results due to not all clients being required for model training (e.g., a client - and its data - is intermittently accessible for training due to networking).  release_notes_warning_nvflare_minclients.png

GCP Marketplace Listing

Following the partnership announcement with Google Cloud, Rhino Federated Computing Platform can now be purchased directly on GCP Marketplace. See more information directly in Rhino's listing on the GCP Marketplace.

May 2024

NVFlare 2.4

Rhino FCP now support NVIDIA’s latest version of FLARE - version 2.4. There are many enhancements that have been added to this release, see a detailed list in NVFlare documentation. With Rhino FCP, you should be ready to use these new functionality today. To get started, see this example NVFlare 2.4 model in the user resources.

Datasets from external storage

Importing datasets from external storage now also supports importing from SMB, in addition to the existing AWS S3 and GCS import functionality. Additionally, Rhino FCP now also supports exporting datasets to external storage (including S3, GCS, and SMB). See more details in the documentation

Using LLMs with Rhino FCP

We’ve added new examples to our user resources Github repo for how to use LLMs in interactive containers:

Remote annotation workflow enhancements

When using a Secure Access List (SAL) as the input for an interactive container run, the output dataset will use the original dataset’s name as its prefix by default vs. the previous default of using the SAL’s name. This makes it easier to run consecutive interactive container sessions while maintaining a consistent name for the generated datasets.

Rhino Health’s 3D Slicer interactive container now overcomes the issue of storing output files when overriding soft links.

April 2024

Import data in your cloud storage (AWS S3 or GCP Cloud Storage)

The Rhino Health FCP now provides an additional way to make data accessible to the platform for importing into your projects. If you use AWS or GCP within your environment, you can import data from one or more AWS S3 buckets or GCP Cloud Storage buckets in your network. Any data you put in these buckets will be available for importing via FCP. Since such buckets and the Rhino Client are in your environment, data always stays local to your environment and is never sent to an external location. Please contact us if you'd like to set up access to cloud storage from your Rhino Client. See more details in the documentation

 

Rank-Based Federated Metrics

Spearman's Rank Correlation and Wilcoxon Signed Rank Test are now also supported on FCP. As with other federated statistical methods on the platform, these metrics can be calculated in a federated and privacy preserving manner across multiple datasets, adhering to the different privacy and permission safeguards built into the FCP. These rank-based metrics utilize a novel federated and privacy preserving ranking algorithm that was developed and implemented on FCP, and validated on a large number of datasets. See more details in the documentation.

 

March 2024

Run-time External Files

Large files (such as LLM parameter files) can now be accessed by Code Objects at runtime without the need to include them in the container image that is built and pushed to the FCP. Users can now upload such files to a dedicated cloud storage bucket, select which files to make accessible when running the code, and then reference them directly in the code during run time (on the Rhino Client). This feature is available in all FCP interfaces, namely API, SDK, and GUI. See more detail in the documentation.

Additional Federated Metrics

Pearson Correlation and Intraclass Correlation are now available as federated metrics on FCP. As with other federated biostatistics on the platform, these metrics can be calculated in a federated and privacy preserving manner across multiple datasets, adhering to the different privacy and permission safeguards built into the FCP. 

GUI Enhancements

Multiple UX and UI enhancements in the GUI, including:

  • Improved file upload experience when creating Python Code Objects - in Standalone File mode you can now upload files directly from your local file system instead of pasting their contents into the UI
  • Improved experience when creating Federated Datasets - any errors will be surfaced directly in the Federated Dataset creation dialog
  • More informative messaging when adding collaborators - after adding a collaborator to a project, a message will be shown until the collaborator accepts the project permissions and joins the project
  • Improved user messaging when loading TensorBoard, importing Datasets, and creating Code Objects
  • Better handling of long project names in the UI

February 2024

Additional Federated Metric: Cox Proportional Hazards

After extensive research and implementation, we have added support for federated and privacy-preserving calculation of Cox Proportional Hazards. This metric can be now calculated across multiple sites without the need for centralizing the data, allowing for robust federated survival analysis. Existing privacy preservation mechanisms such as K-Anonymity and Differential Privacy are fully supported. This adds to our set of federated metrics for statistical analysis, biostatistics, and epidemiology research. To learn more, check out our documentation or reach out to us!

 

Platform Terminology Renaming

We have renamed concepts in FCP for better consistency with industry terms and for improved clarity. These changes are applied consistently across the graphical interface (GUI), software development kit (SDK), and backend API. The main name changes are:

  • Cohorts to Datasets: We've updated "Cohorts" to "Datasets" to better align with industry standards, ensuring a more accurate representation of the datasets within Rhino Health FCP.
  • Models to Code Objects: The overarching term "Models" has been rebranded as "Code Objects." This term encompasses a variety of containers, including Generalized Compute containers, Python Code / Auto-containers, Interactive Containers, and NVFlare containers. For brevity, it may be abbreviated as "Code" where suitable.
  • Model Results to Code Runs: We've transitioned from "Model Results" to "Code Runs" to accurately represent the outcomes of code execution and providing a clearer understanding of the results generated.

This will help streamline the terminology and make it more intuitive for users.  We have outlined all of the changes that occurred within our Documentation Center: FCP Terminology Renaming Guide.

 

January 2024

Facilitating Collaborative Research with Federated Datasets

The Federated Datasets (FDs) feature makes exploration of and collaboration with data distributed across multiple partners seamless. FDs allow hospitals or other data custodians to quickly allow researchers at other organizations to access curated datasets to determine if they would be useful for that researchers’ project - and then just as easily incorporate them into their projects. The hospital has complete control over what datasets are imported and who can view the summary statistics - accelerating the time to innovation while preserving the data custodian’s control. 

 

2023 Release Notes

The following contains the release notes from 2023, in descending order.

December 2023

Auto-Containers

The Auto-Containers feature allow users to generate automatically containers of python code. This feature allows our users to:

  • Upload for multiple files per container, including code and binary files. We have expanded file compatibility to containerize both textual and binary files.
  • Customize Python and CUDA versions for automatically creating the right python environment for your code.
  • Manage code packages not only with pip, but now also with Conda.

Enhancements in Federated Biostatistics

We have developed methods for federating more ‘traditional’ biostatistics that have not been previously amenable to federation. Supported metrics include Welch’s t-test, Chi-squared test, and ANOVA. We have also included support to calculate these metrics on Federated Datasets to easily enable reporting cross-site federated analysis on critical biostatistics.

Federated Join

New support for cross-site metrics in projects where the same patient’s data may be spread across multiple sites. This is achieved without moving the cohort data into a central location, while giving the same functional result as if these datasets were joined together in the cloud. This new capability is supported for the Count, Sum, Mean, and StandardDeviation metrics in the Rhino SDK, and works in concert with existing privacy preservation mechanisms in FCP.

 

October 2023

  • Rhino Health FCP Model Run Logs Redesign
    • Introducing a redesigned Model Run Logs view in the Rhino Health Federated Computing Platform (FCP) to enhance the debugging experience for data scientists. This improved interface empowers users to efficiently identify and resolve issues during their project workflows.

    • Key Features:

      • New “General Info” tab section: You can now access all the relevant information regarding the specific model and model run from within the Logs view.
      • Clear and Comprehensive Log Organization: The Logs view now thoughtfully organizes logs, distinguishing between FCP-generated logs and client-side logs generated by participating sites. This clear separation allows users to quickly pinpoint the source of errors and diagnose issues with ease.
      • Improved Federated Learning Monitoring: For federated learning runs, the Logs view effectively distinguishes between FCP-generated logs, FL-server logs, and individual logs for each FL-client. This detailed breakdown allows users to monitor server-side activities and individual client-side interactions, facilitating debugging and fine-tuning of models and optimizing performance in collaboration with project partners.
    • The redesigned Model Run Logs view ensures a seamless debugging flow, facilitating data scientists' work on healthcare data while preserving privacy and enabling secure collaboration. We are committed to continuously improving the Rhino Health FCP to provide you with the best distributed computing and federated learning experience in healthcare. Happy debugging!

  • Additional Upgrades
    • Updated the documentation link in the dashboard to link to docs.rhinohealth.com
    • Increased the export cohort path limit from 100 to 250 characters
    • Added an indication on the cohorts pages regarding the workgroup that the cohort belongs to (which may be different from the “Source” which is the person who triggered GC, performed annotation, etc.)
    • The “(X visible)” text on the cohorts list page will only display if there is a cohort filter active
    • A new permission setting for Differential Privacy with the title “Level of enforcement of differential privacy”, accepting the values 0, 1, 2, or 3 (with 2 being the default) was created. A new tooltip contains a detailed explanation of what each value means. This will impact whether noise will be added to different metrics calculated from cohorts in the project.
    • There is a new user example within the user-resources GitHub under /examples/nvflare for Generalized Linear Models (GLM), including a notebook showing how to trigger this model and read the resulting coefficients and standard errors.

September 2023

  • Federated percentiles with differential privacy
    • We are thrilled to announce that Rhino Health's Federated Computing Platform has undergone an exciting enhancement, now enabling the calculation of percentiles for cohorts and federated datasets. This feature empowers you to derive valuable insights from your data while preserving patient data privacy.

    • Key Features:

      • Differential Privacy Integration: To fortify patient data privacy during percentile calculations, we have integrated enhanced support for Differential Privacy into our federated analytics capabilities. This addition serves as an additional layer of protection alongside k-anonymization.
      • User-Controlled Privacy: You, our valued users, now have granular control over the enforcement of differential privacy within your projects. This means you can tailor the privacy settings according to your project's specific requirements, ensuring the utmost data security.
      • Expanded Analytical Possibilities: With this new feature, you can effortlessly compute descriptive statistics such as minimum, maximum, median, and other percentiles. Rhino Health's commitment to patient data privacy remains unwavering, even as we broaden your analytical horizons.
    • Experience the Upgrade:

      • We believe this update will significantly augment your data analysis capabilities while upholding the highest standards of data privacy. As always, we value your feedback, so please don't hesitate to reach out with any questions or comments.

August 2023

  • TensorBoard Integration
    • We are excited to announce the latest enhancement to the Rhino Health Federated Computing Platform (FCP)! With our new integration of TensorBoard, data scientists and researchers can now seamlessly monitor their model training metrics during and after federated training runs, further enhancing the collaborative and privacy-preserving nature of the FCP ecosystem.

    • Key Features:

      • Real-time Insights: The integration with TensorBoard empowers users to gain real-time insights into their federated machine learning model training processes. Visualize crucial metrics as they evolve, enabling quicker decision-making and optimization strategies.
      • Federated Server-side Metrics: Keep a close watch on the aggregated performance metrics of the federated model across all participating institutions. Monitor how the model evolves as data from different sites contribute to its refinement.
      • Federated Client-side Metrics: Dive deep into the performance of your model at individual client sites. Compare how different institutions' data affect the training outcomes, helping you understand the nuances of multi-site data distribution.
      • Enhanced Collaboration: The integration fosters enhanced collaboration by allowing users to share training metrics and insights seamlessly with their collaborators. Discuss strategies, refine models, and drive innovation while maintaining data privacy behind hospital firewalls.
      • Performance Comparison: Easily compare model performance between different federated training runs, enabling you to track progress, identify trends, and make informed decisions about optimization techniques.
      • Streamlined Workflows: The Rhino Health FCP's integration with TensorBoard aligns seamlessly with your existing workflows. Monitor model training metrics without disrupting your established processes, making the transition smooth and intuitive.
    • This new integration is a significant step forward in expanding the capabilities of the Rhino Health FCP, reinforcing our commitment to enabling collaborative, secure, and privacy-conscious healthcare research. Whether you are fine-tuning machine learning models using data from a single site or performing federated training across multiple distributed data sets, the Rhino Health FCP's TensorBoard integration empowers you to gain deeper insights and drive transformative advancements in healthcare.

  • Enhanced Inferred Schema Capability
    • We are delighted to introduce a significant enhancement to the existing inferred schema capability on the Rhino Health Platform. This improvement elevates your data processing experience by seamlessly integrating your files and DICOM data into your workflows, making your analysis more intuitive, efficient, and insightful.

    • Key Features:

      • Streamlined File/DICOM Data Handling: The updated inferred schema capability extends its prowess to include file and DICOM data, alongside the existing tabular data. The automated schema generation now accommodates "Filename" and "DicomInstanceUID" fields in addition to "String" and "Float" fields.
      • Simplified Cohort Imports: When importing cohorts with file or DICOM data, the platform now empowers you to bypass the need for a CSV file. By selecting the option to "Auto-generate schema from data", you can seamlessly import cohorts with a single column representing the files or DICOM instances, simplifying your data import process.
      • Effortless Schema Mapping: In cases where cohort imports involve both CSV files and file/DICOM data, the improved inferred schema capability smartly maps CSV columns to associated files or DICOM instances using actual filenames and DICOM UIDs. This automation minimizes manual mapping and ensures accurate data integration.
      • Consistent Model Output Schemas: Whether you're running Python Code/GC/iGC, training model, or running inference with NVFlare, the enhanced inferred schema capability ensures automatic schema generation for your model outputs. This eliminates the need for manual schema creation and maintains consistency across your workflow.
    • Experience the Upgrade:

      • Experience the power of the improved inferred schema capability as it seamlessly weaves file and DICOM data into your workflows. The platform's automated schema generation simplifies data handling, enabling you to focus on analysis rather than data organization.

      • From processing DICOM files to model creation, the enhanced capability offers a streamlined experience. Your output schemas accurately represent your data, guaranteeing a seamless transition from input to insightful analysis.

  • Additional Upgrades
    • Increased file path length to 250 characters in Cohort import input fields
    • Renamed the exported schema file name to match the name of the schema
    • Added the ability to remove multiple cohorts or models at once
    • Added support for Ubuntu 22.04
    • Fields on the Schema page were renamed for better user clarity
    • UTC Timestamp labels were added to all pages for consistency

 

July 2023

  • Enhanced Interactive Containers
    • We are thrilled to announce the latest upgrade to Rhino Health Federated Computing Platform's (FCP) powerful feature - Interactive Containers. This enhanced version brings significant improvements to your data interaction experience, empowering you to achieve more with ease.

    • Key Features:

      • Multiple Concurrent Sessions: With the enhanced Interactive Containers, you can now engage in multiple concurrent sessions per client. This upgrade unlocks a new realm of collaboration, particularly for projects that require parallel Interactive Container sessions like distributed annotation tasks. Seamlessly collaborate with your team members, leveraging the power of concurrent sessions to accelerate progress and drive results.
      • Improved Work Saving: We've listened to your feedback and made significant enhancements to the work-saving functionality in Interactive Containers. Previously, saving your work during an Interactive Containers session required terminating the session. Now, the upgraded Interactive Containers system enables you to easily save your work within the session itself. This newfound flexibility ensures that your progress is securely preserved, minimizing the risk of losing unsaved work and providing peace of mind as you work with distributed data.
    • At Rhino Health, we are committed to refining your data interaction experience and providing you with a seamless and secure environment. The enhanced Interactive Containers feature is a testament to our dedication to your success. Unlock new levels of collaboration, efficiency, and productivity as you leverage the power of Interactive Containers within the Rhino Health FCP.

    • For more information on how to use Interactive Containers, please refer to our documentation in Generalized Compute User Guide. We hope you find this new feature helpful and welcome any feedback you may have.

  • Data Ingestion from SQL
    • We're excited to announce the latest addition to Rhino Health Federated Computing Platform's (FCP) capabilities - Data Ingestion from SQL. With this feature, data scientists can now seamlessly access and import data from on-prem SQL databases external to the platform, streamlining their workflows and opening up a world of new possibilities for collaborations.

    • Key Features:

      • Rhino SDK SQL Connector: The initial release includes an SDK connector that allows users to connect to an on-prem SQL database securely. The connector facilitates data retrieval without the need to move data outside of the firewall.
      • Query and Aggregate Statistics: Data scientists can run queries on the remote SQL database, perform actions such as calculating summary statistics (e.g., count, mean, standard deviation) in a privacy-preserving manner, and explore the data to identify the exact parameters needed for their cohort.
      • Import Cohorts with Ease: Once the desired cohort is identified, the Rhino SDK function enables effortless data extraction and seamless import of the cohort into FCP, either using a pre-defined schema or an inferred one.
    • Unlock the potential of your on-prem SQL data and collaborate with confidence. Try out Data Ingestion from SQL today!

  • NVFlare Enhancements
    • We are excited to announce two powerful enhancements to the Rhino Health Federated Computing Platform's NVFlare integration. With the support for multiple model weight checkpoint files during training and the new Inference workflow, data scientists can now take their machine learning model development to new heights.

    • Key Features:

      • Multiple Model Weight Support: Train your models with confidence by saving multiple model weight checkpoint files during the training process. This enhancement ensures you capture optimal model performance at various training iterations, allowing for robust single-site or federated learning (FL) training scenarios.
      • Inference Workflow: Seamlessly run inference using any of the saved model weight checkpoints through our intuitive GUI and SDK. Analyze and compare the performance of your models with ease, empowering multi-site development and hyperparameter tuning experiments.
    • Take control of your model development and fine-tune it with precision. Embrace the power of NVFlare enhancements on the Rhino Health Federated Computing Platform today!

    • For more detailed information, we encourage you to refer to the user documentation in Step 5: Halting your model run in the Using NVIDIA FLARE with the Rhino Health Federated Computing Platform document & the RH FCP SDK documentation

  • Squashed some bugs

June 2023

  • Enhanced Python Containers
    • We are excited to announce a new feature on the Rhino Health FCP - "Enhance Python Containers". This latest functionality allows users to easily create working models on the platform by simply providing a .py file and a requirements.txt file. The system will then automatically create and push the container image and make the model available for immediate use on the FCP.

  • Halting Model Training through the SDK and GUI
    • We are excited to announce a new feature on the Rhino Health FCP - “Halt model training”. This latest functionality enhances the FCP's support for the end-to-end data science workflow by allowing users to easily halt ongoing federated training runs via the Rhino Health GUI with a single click or via a simple SDK call. 

    • For more detailed information, we encourage you to refer to the user documentation at NVFlare User Guide & SDK Documentation

  • Squashed some bugs

May 2023

  • Interactive Permissions Setting
    • We are pleased to introduce a new enhancement to the Permissions on the Rhino Health FCP - This latest enhancement enables project leads to define the fine-grained permissions policy for the project and for their site when creating a new project, without the need to request changes from the Rhino Health support team.

    •  
    • How does this work?

    •  
    • As a Project Owner - Watch our Video Tutorial

      When creating a new project, you will see a new “Permissions Policy” section in the project creation dialog where you can set the project-level and site-level permissions for the lead site.

    • As a Collaborator on a Project - Watch our Video Tutorial

      When a new collaborator is invited to the project, they will now receive an email invitation with a link to the project.

      When the new collaborator logs into the project for the first time, they will be able to set their site-level permissions on the project and will need to approve the project-level permissions policy in order to gain full access to the project.

    •  
    • Please note that changing the permissions policy after the initial setting is currently only possible by contacting support@rhinohealth.com.

    • For more information on how permissions work on FCP, please refer to our Rhino Health Federated Computing Platform User Manual. We hope you find this new feature helpful and welcome any feedback you may have.

  • Squashed some bugs

April 2023

  • Interactive Containers
    • Rhino Health FCP is pleased to announce the launch of a new feature - Interactive Containers. This powerful and versatile capability serves as a secure remote desktop, allowing users to interact with distributed data in their projects via remote, interactive graphical user interfaces (GUIs) such as Jupyter Notebook and 3D Slicer.

    • With Interactive Containers, users can define their own custom Interactive Containers to use their preferred third-party tools, such as annotation tools. This new feature offers users increased flexibility and efficiency when working with distributed data and tools while maintaining high levels of security. We are thrilled to offer Interactive Containers as a valuable addition to the Rhino Health FCP.

    • For more information on how to use Interactive Containers, please refer to our documentation in the Generalized Compute User Guide. We hope you find this new feature helpful and welcome any feedback you may have.

  • Data Exploration Dashboard
    • We're thrilled to introduce our latest enhancement to the Cohort Analytics view on the Rhino Health FCP. With this new feature, users can interactively explore their project's data with greater granularity by defining filters on the values to be presented. The charts are updated automatically in the GUI, providing greater insights into the data.

    • We appreciate your continued support and hope you enjoy this new addition to the Rhino Health FCP. Try out the new Data Exploration Dashboard today and take your data exploration to the next level!

  • Permissions UI
    • Our permissions system has been designed to be highly flexible, allowing project owners to define permissions down to the individual activity level. This means that users can specify exactly which features each participant is allowed to access, ensuring that your project data remains secure and protected from unauthorized access.

    • To make it easier for project participants to manage their permissions, we have introduced the first phase of a new Permissions UI on the Collaborators Page. With this interface, users can quickly view the permissions that have been granted to each persona, helping to prevent errors and mistakes that could compromise your project data.

    • We believe that this new permissions UI represents a significant first step forward in project management and collaboration, and we are confident that our users will appreciate the added visibility it provides. If you have any questions or feedback, please don't hesitate to reach out to our support team at support@rhinohealth.com.

  • Minor Feature Releases/Bug Fixes
    • In the latest version of the NVFlare docker-run.sh script, we have implemented a new feature to disable networking on the running container. This is designed to replicate the behavior of the container when running within FCP, effectively blocking any attempts to access the internet.

    • We have introduced a new feature to filter out repetitive NVFlare SSL_ERROR messages. This is aimed at reducing the amount of log clutter caused by these messages, making it easier to identify and address other issues in the logs. With this update, users can now enjoy a cleaner and more streamlined logging experience, with only the most relevant messages being displayed.

    • We have made improvements to our built-in OHIF viewer, including enhanced support for multiple annotations in a single instance. Additionally, we have streamlined the workflow for adding annotations and textual notes, making the process faster and more intuitive for users. With these improvements, users can enjoy a more efficient and user-friendly experience when working with our OHIF viewer.

March 2023

  • Credentials via the GUI
    • Users can now access their credentials to additional tools/systems utilized alongside FCP (e.g. SFTP, ECR) via the Profile page in the FCP GUI.

    • We found our users often found it challenging to keep track of the various credentials needed for different parts of the workflow (SFTP to transfer files into the on-prem client, ECR to push model containers to the cloud).

    • With this new release, users can easily access their credentials on the GUI at any time.

  • MFA-gated access
    • We are pleased to announce the release of a new feature on the Rhino Health FCP - MFA-gated access. With this feature, institutions can now ensure an additional layer of security for their data by requiring external collaborators to have Multi-Factor Authentication (MFA) defined on their FCP account before accessing the institution's data.

    • We believe that the new MFA-gated access feature on the Rhino Health FCP provides healthcare institutions with added peace of mind regarding the security of their data. By requiring external collaborators to have MFA defined on their FCP account, institutions can ensure that only trusted parties are able to access their data, thus adding an extra layer of security to the platform. This feature further strengthens the Rhino Health FCP's position as a reliable and secure option for healthcare institutions.

    • To enable this feature for your institution, please contact our support team at support@rhinohealth.com. Our team will assist you in configuring MFA-gated access and ensuring seamless collaboration between your institution and external collaborators.

  • K-per project
    • We are excited to announce the release of “K-per project”, a new feature now available on the Rhino Health FCP. With this latest enhancement, project leads can now set the K-anonymization parameter explicitly while creating a new project, providing greater control over privacy requirements.

    • Previously, the K parameter was hard-coded as a constant for all FCP projects, but with the new K-per-project feature, our customers can easily adjust the K-anonymization setting based on their specific needs. By setting K-anonymization, subgroup summary statistics will only be provided on the FCP for groups with greater than or equal to K items. The K-per project feature allows for greater flexibility and can be disabled by setting K=1.

    • We believe that the K-per project feature will help our customers maintain a higher level of privacy control and meet their unique data privacy needs on the Rhino Health FCP.

  • GUI Usability Improvements
    • We are delighted to announce the release of a set of usability enhancements to our Rhino Health FCP GUI. The new features are designed to simplify user workflows and improve the overall user experience.

    • Cohort View Filters: For projects containing a large number of cohorts, locating a specific cohort or group of cohorts can be a daunting task. With our new cohort view filters, users can easily limit their search by Schema and the cohort's Source, making it easier to find the desired information.

    • Cohort Analytics: To facilitate searching within the cohorts filter, we have integrated search functionality into the GUI. This feature will enable users to efficiently search and display the desired cohort(s) they are looking for.

    • Cohort Import Dialog: In our efforts to improve user experience, we have added a pre-populated base path /rhino_data/ to the cohort import dialog box. This new hint will assist users in placing their files correctly on their on-premises clients, making it easier to import cohorts into the platform.

 

Was this article helpful?
0 out of 0 found this helpful

Articles in this section