HOPPR to Showcase Pioneering Catalyst Research Projects at SIIM26, Open Applications for New Cohort

Program participants at MAI Lab Lagos, The Catholic University of Korea, and the University of Illinois Cancer Center are using the HOPPR^® AI Foundry to advance projects spanning breast cancer detection, lung cancer prediction, and fracture detection.

• HOPPR's Catalyst Program is now open for applications, helping radiologists, researchers, and healthcare organizations turn promising imaging AI ideas into real-world projects.
• Initial Catalyst participants from MAI Lab Lagos, The Catholic University of Korea, and the University of Illinois Cancer Center are advancing research focused on breast cancer detection, musculoskeletal findings on chest X-rays, and cancer risk prediction.
• SIIM26 attendees can learn more about the program, explore participant projects, and learn how to apply for the next cohort at HOPPR's booth, #509.
• HOPPR is showcasing interactive HOPPR^® AI Foundry demos at booth #509 and supporting SIIM's AI Play Station experience at Kiosk 1 on Startup Street, where attendees can fine-tune a chest radiography foundation model live.

CHICAGO, June 10, 2026 /PRNewswire/ -- HOPPR, a company transforming how AI is developed for medical imaging, announced today that applications are now open for the next cohort of its Catalyst Program, in conjunction with the SIIM26 Annual Meeting + Informatics TECH Expo (SIIM26), June 10-12, 2026.

HOPPR's Catalyst Program provides clinician scientists, imaging researchers, and AI developers with early access to HOPPR foundation models, curated datasets, fine-tuning tools, compute resources, training, and support from HOPPR's machine learning scientists. Enabled by the HOPPR^® AI Foundry, the research program helps participants move from ideas to results more efficiently by eliminating common barriers such as fragmented data, infrastructure overhead, and inconsistent development practices.

"One of the biggest challenges in healthcare AI isn't a lack of ideas but giving clinicians and researchers the tools to actually test and build them," said Khan Siddiqui, MD, Founder and CEO of HOPPR. "The Catalyst Program was designed to lower those barriers by providing access to foundation models, infrastructure, and expert support in one place. We're excited to open applications for the next cohort and welcome more clinicians, scientists, and engineers who want to explore new ideas and advance medical imaging AI responsibly and collaboratively."

The first Catalyst Program cohort includes projects spanning breast imaging, chest radiography, and cancer risk prediction:

• Researchers at MAI Lab Lagos are using the HOPPR^® AI Foundry to develop and fine-tune mammography AI models using Nigerian mammography datasets, with the goal of improving early breast cancer detection in Africa and advancing more equitable AI development.
• A research team at The Catholic University of Korea is adapting a chest X-ray foundation model to determine whether routine chest radiographs can be used to identify musculoskeletal abnormalities, including thoracic spine findings.
• Investigators affiliated with the University of Illinois Cancer Center are fine-tuning HOPPR's chest CT and mammography foundation models to explore how imaging AI can help predict future lung cancer and breast cancer risk.

"Participating in HOPPR's Catalyst Program was a genuinely valuable experience for our team," said Sungwon Lee, MD, PhD, Assistant Professor and Co-Director of VALID Lab at Seoul St. Mary's Hospital, The Catholic University of Korea. "As radiologists, we often encounter clinical questions that seem well-suited for AI, but moving from an idea to a working model can be difficult because of the technical barriers involved. The HOPPR^® AI Foundry helped lower many of those barriers and allowed us to focus on the clinical question itself. For clinicians with meaningful imaging data but limited engineering resources, platforms like this can make it much easier to explore and test AI ideas that might otherwise never get off the ground."

"HOPPR's streamlined platform got us from concept to a prototype model in record time — we achieved in days what would have taken months," said Ameen Salahudeen, MD, PhD, Assistant Professor at the University of Illinois Cancer Center.

"HOPPR bridges the gap between clinical and technical expertise required to develop ML methods for healthcare applications," said Udunna Anazodo, Ph.D., Scientific Director of MAI Lab Lagos and Assistant Professor at McGill University.

Attendees can visit HOPPR at booth #509 to learn more about the Catalyst Program and apply for the next cohort. HOPPR will also be showcasing interactive demonstrations of the HOPPR^® AI Foundry at its booth and supporting SIIM's AI Play Station experience at Kiosk 1 on Startup Street, where attendees can fine-tune a chest radiography foundation model live using the HOPPR^® AI Foundry.

Those unable to attend can learn more about the Catalyst Program and apply at www.hoppr.ai/build/catalyst-program. HOPPR will also host an informational webinar for the Catalyst Program in July, featuring select participants and projects from the first cohort.

About HOPPR

Founded in 2019, HOPPR brings together experts in clinical radiology, AI development, and healthcare commercialization to advance the development of transparent and scalable AI for medical imaging. The HOPPR® AI Foundry is a secure development platform designed for building, fine-tuning, validating, and hosting AI models for medical imaging. The platform provides curated datasets, traceable development workflows, and secure infrastructure that support responsible AI development aligned with industry quality and regulatory standards. HOPPR AI Foundry holds SOC 2 Type II attestation and HITRUST e1 Certification and is HIPAA-compliant. HOPPR also offers Forward Deployed Services, embedding HOPPR's machine learning scientists, software engineers, data scientists, and clinical experts directly within your team to accelerate development at every stage. For more information, visit www.hoppr.ai.

SOURCE HOPPR