working-groups.html

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              Working Groups
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                MONAI Working Groups are specialized teams that drive innovation and progress in specific areas of medical imaging AI. 
                Each group brings together experts from diverse backgrounds to address key challenges and opportunities in their respective domains.
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                These groups focus on developing solutions, establishing standards, and creating resources that benefit the entire medical 
                imaging AI community. From technical implementation to clinical applications, our working groups ensure that MONAI remains 
                at the forefront of healthcare AI innovation.
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                Working group members collaborate regularly through meetings, workshops, and joint projects, fostering knowledge exchange 
                and driving continuous improvement in their specialized areas.
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
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                      This working group aims to define how to close the existing gap from research and development to clinical production environments by bringing AI models into medical applications and clinical workflows with the end goal of helping improve patient care. The focus includes defining the open high-level functional architecture and determining which components and standard APIs are required. By collaborating with the MONAI developers, the group will move from requirements to implemented solutions.
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                            <p class="font-medium text-gray-800 mb-1">Barbaros Selnur Erdal</p>
                            <p class="text-sm text-gray-600">Mayo Clinic</p>
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                            <p class="font-medium text-gray-800 mb-1">Haris Shuaib</p>
                            <p class="text-sm text-gray-600">Guy's and St Thomas' NHS Foundation Trust</p>
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
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                      This working group aims to establish and maintain technical excellence in MONAI Core by coordinating development efforts and ensuring high code quality standards. The focus includes overseeing architectural decisions, establishing development guidelines, and maintaining technical documentation with the end goal of creating a robust and sustainable framework for medical imaging AI research and development. By fostering collaboration between contributors, the group ensures consistent implementation of best practices across the codebase.
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-4">GROUP LEADS</h4>
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                            <p class="font-medium text-gray-800 mb-1">Nic Ma</p>
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                            <img src="assets/img/EricKerfoot.png" alt="Eric Kerfoot" class="w-24 h-24 rounded-full object-cover">
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                            <p class="font-medium text-gray-800 mb-1">Eric Kerfoot</p>
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                            <p class="font-medium text-gray-800 mb-1">Yun Liu</p>
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                        <span>Learn More</span>
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
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                      This working group aims to accelerate the adoption of AI in medical imaging through comprehensive educational resources and training materials. The focus includes developing structured learning paths, hands-on tutorials, and practical exercises with the end goal of enabling researchers and clinicians to effectively utilize MONAI in their work. By creating accessible content for various skill levels, the group helps bridge the knowledge gap in medical imaging AI.
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-4">GROUP LEADS</h4>
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                            <p class="font-medium text-gray-800 mb-1">Eric Kerfoot</p>
                            <p class="text-sm text-gray-600">King's College London</p>
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                            <p class="font-medium text-gray-800 mb-1">Marc Modat</p>
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
                    <p class="text-gray-700">The Evaluation and Benchmarking MONAI working group aims at providing guidelines, infrastructure, and practical tools for evaluation and benchmarking of medical image analysis methods. It focuses on leading the community towards the identification and adoption of best practices for evaluation and benchmarking and on identifying practical solutions to improve reproducibility.
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                            <p class="font-medium text-gray-800 mb-1">Annika Reinke</p>
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                            <p class="font-medium text-gray-800 mb-1">Carole Sudre</p>
                            <p class="text-sm text-gray-600">UCL</p>
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
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                      This working group aims to advance collaborative medical AI research through secure and efficient federated learning implementations. The focus includes developing standardized workflows, ensuring data compatibility, and creating modular components with the end goal of enabling distributed learning across institutions while preserving data privacy. By establishing best practices for federated learning, the group facilitates multi-institutional collaboration in medical AI research.
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                            <p class="font-medium text-gray-800 mb-1">Holger Roth</p>
                            <p class="text-sm text-gray-600">NVIDIA</p>
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                            <p class="font-medium text-gray-800 mb-1">Sai Praneeth Karimireddy</p>
                            <p class="text-sm text-gray-600">USC</p>
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              <!-- Human-AI Interaction Card -->
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
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                      This working group aims to advance, standardize, and support human-AI interaction cycles through well-defined interfaces and community-driven development. The focus includes developing standardized APIs for AI prompting, uncertainty communication, and annotation feedback processes with the end goal of creating robust, reusable interactive AI workflows. By fostering community-driven specification and implementation across diverse domains including radiology, pathology, and surgery, the group enables efficient AI deployment across cloud, local, and HPC environments.
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-4">GROUP LEADS</h4>
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                            <p class="font-medium text-gray-800 mb-1">Dr. Ralf Floca</p>
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                            <p class="font-medium text-gray-800 mb-1">Dr. Saad Nadeem</p>
                            <p class="text-sm text-gray-600">Memorial Sloan Kettering Cancer Center</p>
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                            <p class="font-medium text-gray-800 mb-1">Bruce Hashemian</p>
                            <p class="text-sm text-gray-600">NVIDIA</p>
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                        <span>Learn More</span>
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              <!-- Ophthalmology Card -->
              <div id="ophthalmology" class="bg-white rounded-xl shadow-md scroll-mt-24">
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
                    <p class="text-gray-700">
                      This working group aims to advance the application of AI in ophthalmology through specialized tools and algorithms. The focus includes developing analysis methods for various ophthalmic imaging modalities, creating annotation tools, and building predictive models with the end goal of improving diagnosis and treatment of eye diseases. By leveraging MONAI's capabilities, the group accelerates innovation in ophthalmic image analysis.
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-4">GROUP LEADS</h4>
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                            <p class="font-medium text-gray-800 mb-1">Jayashree Kalpathy-Cramer</p>
                            <p class="text-sm text-gray-600">University of Colorado</p>
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                            <img src="assets/img/LeeAaron.png" alt="Aaron Lee" class="w-24 h-24 rounded-full object-cover">
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                            <p class="font-medium text-gray-800 mb-1">Aaron Lee</p>
                            <p class="text-sm text-gray-600">University of Washington</p>
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                    <a href="wg_ophthalmology.html" class="px-6 py-2.5 rounded-lg bg-white text-brand-primary border-2 border-brand-primary hover:bg-brand-light transition-colors inline-flex items-center gap-2 group">
                        <span>Learn More</span>
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              <!-- Outreach Card -->
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                    <h4 class="uppercase text-sm font-medium text-gray-600 mb-3">MISSION</h4>
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                      This working group aims to expand MONAI's reach and impact across the medical imaging community. The focus includes organizing technical training, fostering community engagement, and developing infrastructure with the end goal of establishing MONAI as the leading open-source platform for medical imaging AI. By building an inclusive and transparent community, the group ensures sustainable growth and adoption of MONAI.
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                        <span>Learn More</span>
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