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Digital Pathology and AI Congress 2026. April 10, 2026 May 7-8, 2026 | Columbus, Ohio. Kitware is heading to Columbus, Ohio, on May 7-8 to exhibit at the Digital Pathology & AI Congress. This event brings together experts across pathology, artificial intelligence, and biomedical research to explore how digital technologies are transforming the study and diagnosis of disease. As AI and whole-slide imaging technologies continue to mature, the focus is shifting from innovation to implementation. Many organizations are no longer asking whether these technologies work, they are focused on applying them effectively within existing environments. The challenge lies in scaling solutions, connecting them with existing platforms, and ensuring consistent performance across complex systems. At this year's Congress, Kitware is focused on helping organizations address these challenges by turning AI models into scalable, production-ready systems. From prototype to production in Digital Pathology. Most teams can develop AI models. Far fewer can turn those models into systems that operate reliably at scale. Kitware partners with organizations to bridge the gap between research and deployment, helping teams move from early-stage development to production-ready systems. Kitware Inc. design and build high-performance systems that: * Integrate AI into existing platforms without requiring a full rebuild. * Scale to support large whole-slide imaging datasets. * Enable multi-user workflows and evolving system requirements. Digital pathology systems must also evolve as data volumes, users, and requirements grow. They need to integrate with existing tools while avoiding long-term technical constraints. Its approach enables organizations to: * Integrate AI/ML into existing products without rebuilding their platforms. * Optimize performance for large WSI datasets and multi-user environments. * Maintain control through open, extensible architectures. * Support long-term system evolution with ongoing technical leadership. Unlike rigid or closed platforms, its systems are designed to integrate with your existing infrastructure and evolve with your needs over time. If you're building a digital pathology platform, developing AI-driven applications, or scaling research workflows, Kitware can help you create end-to-end systems. From data ingestion and annotation to model deployment and visualization, designed for real-world use. Proven expertise in Digital Pathology. Effective implementation requires deep domain expertise in whole-slide imaging, AI pipelines, and digital pathology workflows. Kitware's work is grounded in proven platforms like HistomicsTK and extended through custom architectures designed for performance, scalability, and long-term product growth. Kitware Inc. also support reproducible, auditable workflows aligned with regulated research and clinical environments and work with organizations to meet specific validation and compliance requirements. Insights from its work. As organizations operationalize AI in digital pathology, several consistent challenges emerge, well beyond model development. The primary challenge is integration. Applying AI in practice requires managing large, complex datasets, supporting flexible deployment across environments, and building workflows that are both scalable and usable for researchers and clinicians. This is especially clear with the rise of foundation models. While they offer powerful capabilities for analyzing whole-slide images, their impact depends on how easily teams can evaluate and incorporate them into existing workflows. With the right tooling, teams can compare approaches, adapt models to their data, and move from experimentation to applied use. At the same time, the scale and complexity of pathology data continue to grow. Efficient access to whole-slide images, along with strong visualization and data management capabilities, is essential for maintaining performance and supporting growth. As collaboration expands, protecting patient privacy remains critical. Automated de-identification helps remove sensitive information from images and metadata, enabling secure data sharing while maintaining compliance and supporting ongoing research. Let's connect. Planning to attend the Digital Pathology & AI Congress? Visit Kitware at the event or reach out in advance to start the conversation. Whether you're exploring new capabilities or looking for a long-term technical partner, Kitware can help you move from prototype to production. Your data is safe with Kitware Inc.! Kitware Inc. do not sell personal information.

IEEE International Symposium on Biomedical Imaging (ISBI) 2026. April 6, 2026 April 8 - 11, 2026 | London, UK. Kitware is excited to announce its participation in the IEEE International Symposium on Biomedical Imaging (ISBI) 2026, taking place in London, UK. ISBI brings together experts working on the theory, algorithms, and computational methods that power modern biomedical imaging - from microscopic analysis to whole-body systems. The conference creates a space where different imaging disciplines connect, exchange ideas, and push the field forward through collaboration. Kitware's activities and involvement. Segmentation from Partial Views via Patient-specific Shape Priors Thursday, April 9 at 10:50 AM (BST) Authors: Jared Vicory, Dženan Zukić, Balazs Vagvolgyi, Peter Kazanzides, Andinet Enquobahrie, Emad Boctor Kitware Inc. is working on a problem that requires estimating the full 3D shape of the prostate from ultrasound images that only capture part of the organ. Its approach combines a standard segmentation approach on the visible region with a shape model to estimate the missing parts of the boundary. This model has two components: a pre-trained model built from a large population of prostate shapes, and a patient-specific adjustment derived from a pre-operative MRI scan. By combining these sources of information, its method produces more accurate results than traditional segmentation approaches, especially when only a small portion of the prostate is visible. This work was funded by the Advanced Research Projects Agency for Health (ARPA-H) under Award Number D24AC00359-00. The ARPA-H award provided 100% of total costs and total up to $20.9M. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Advanced Research Projects Agency for Health. Advances in shape modeling and 3D reconstruction in medical imaging. Its work builds on a broader set of efforts around using anatomical shape as a meaningful source of information in medical imaging. Shape can capture subtle structural differences that traditional intensity-based approaches often miss, opening the door to earlier detection, better patient stratification, and more personalized care. Through tools like SlicerSALT, Kitware Inc. has explored how analyzing shape across populations can help uncover high-dimensional biomarkers and provide deeper clinical insight. Kitware Inc. also draw on ongoing work in statistical shape modeling, where population-level models are used to understand how anatomy varies and to support applications like 3D reconstruction, predictive modeling, and patient-specific simulation. These approaches combine geometric representations with data-driven methods, making them a strong foundation for many precision medicine workflows. More broadly, this work connects with efforts to reconstruct full anatomical context from partial or limited imaging. Techniques that infer 3D structure from inherently 2D modalities, such as X-ray or ultrasound, are helping improve the accuracy and usability of imaging data for surgical planning and intervention, while also reducing cost and complexity. Partner with Kitware. Kitware Inc. work closely with academic, clinical, and industry partners to solve complex challenges in medical imaging, including segmentation, registration, and multimodal data integration. Through open source development and collaborative R&D, Kitware Inc. help translate advanced imaging methods into scalable, real-world clinical solutions. If you're interested in collaborating or exploring how Kitware can support your research or product development efforts, send Kitware Inc. a message.

Guaranteeing foundation models for high-stakes settings: DARPA's AIQ program. Kitware, a leader in developing software for AI test and evaluation, has been awarded a $3.7M contract to develop a novel AI evaluation framework and perform AI test and evaluation by the Defense Advanced Research Projects Agency (DARPA) for the Artificial Intelligence Quantified (AIQ) program. AIQ will combine theoretical and empirical approaches to assess and understand the capabilities of AI to enable guaranteed performance in domains such as defense, intelligence, manufacturing, and medicine. By providing deeper insights into when these systems can be relied upon and when caution is warranted, the program aims to make high-stakes AI deployment safer, more predictable, and more reliable. Kitware's role: MAGNET for AI evaluation. Kitware will lead test and evaluation (T&E) efforts on the AIQ program as a technical area 2 (TA2) performer, through the development of the Mathematical Assurance and Generative AI Network Evaluation Toolkit (MAGNET). TA1 performers will create mathematical theories and models that predict the outputs of AI transformer models with formal guarantees, providing verifiable performance bounds instead of relying on empirical studies. MAGNET will evaluate TA1 theories at scale by empirically validating their outcome predictions on large, relevant datasets and current, full-scale AI models. To perform this evaluation, MAGNET will provide a framework to ensure that AI systems can be reliably tested across a variety of tasks, datasets, and modalities. MAGNET will leverage open source datasets, models, and tasks where possible, and will employ generative and adversarial techniques to augment evaluation datasets with high-difficulty, out-of-domain problems. Project updates. The Conference on Neural Information Processing Systems (NeurIPS 2025) recently accepted Kitware's workshop paper related to the MAGNET system, which focuses on the design of structured and transparent AI evaluations. Evaluation cards will document the critical metadata, constraints, and claims associated with each AI system, serving as a "contract" between developers and evaluators. * Validate AI system performance empirically, ensuring evaluations are transparent and reproducible. * Generate dynamic benchmarks - including text, image, and multimodal tasks - to test generalization and robustness. * Support scalable inference and testing across CPUs and GPUs in hybrid cloud configurations. * Employ generative and adversarial techniques to create high-difficulty, out-of-domain evaluation datasets. * Provide flexible evaluation workflows that minimize friction for developers while maximizing insight for testers. This work will be presented as a poster at the Evaluating the Evolving LLM Lifecycle workshop at NeurIPS in San Diego in early December 2025, highlighting the team's ongoing research in AI evaluation. Looking ahead. AIQ is a broad, complex program with a large number of performer teams, and it represents an important step toward mathematical guarantees for AI deployment. Kitware's experience in DARPA programs related to explainable and responsible AI positions its team to deliver scalable, reliable, and transparent AI evaluation methods spanning the wide range of TA1 approaches. By creating rigorous, operationally relevant evaluation tools, AIQ aims to answer the fundamental question: when can AI be trusted in high-stakes settings? The outcomes of this program will not only help ensure the safe deployment of AI but also set a new standard for responsible and verifiable AI systems. Kitware: ensuring safe and effective foundation models. Kitware is proud to bring its expertise in AI evaluation to the AIQ program. Through its leadership of the MAGNET effort, Kitware Inc. is developing scalable, transparent, and reliable tools that help ensure AI systems can be trusted in high-stakes scenarios. With experience in other responsible AI programs for DARPA and IARPA, Kitware Inc. continue to advance AI assurance, bridging the gap between research and real-world deployment. Partner with Kitware to elevate the safety, transparency, and trustworthiness of your AI initiatives. Its experts collaborate with organizations to design and deploy AI systems that perform reliably when it matters most. Let's connect to start a conversation about how Kitware Inc. can support your projects and goals. Acknowledgement of Support and Disclaimer This material is based upon work supported by the Defense Advanced Research Project Agency (DARPA) under Contract No. HR001125CE017. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Project Agency (DARPA).