{"id":2519,"date":"2026-05-09T09:20:24","date_gmt":"2026-05-09T09:20:24","guid":{"rendered":"https:\/\/www.phase4ai-project.eu\/?p=2519"},"modified":"2026-05-14T09:36:49","modified_gmt":"2026-05-14T09:36:49","slug":"research-posters-at-medical-image-analysis-mmia-2026-porto-portugal-8-may-2026","status":"publish","type":"post","link":"https:\/\/www.phase4ai-project.eu\/?p=2519","title":{"rendered":"Research posters at Medical Image Analysis (MMIA 2026), Porto, Portugal | 8 May 2026"},"content":{"rendered":"

Research posters at Medical Image Analysis (MMIA 2026), Porto, Portugal | 8 May 2026<\/span><\/p><\/h2><\/div>

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On 8 May 2026, researchers from INESC TEC participated in the 1st Meeting on Medical Image Analysis (MMIA 2026), held in Porto, Portugal, where they presented two posters focused on the application of Generative AI in medical imaging.<\/p>\n

The first work,\u00a0\u201cGenerative AI Meets Super-Resolution: A Unified Pipeline for Cancer Imaging\u201d<\/i>, explored a two-stage AI pipeline combining generative models and super-resolution techniques to improve the quality and availability of breast MRI and lung CT imaging data. The research highlights the potential of synthetic medical imaging to support more reliable diagnostic workflows.<\/p>\n<\/div>

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\u201cGenerative AI Meets Super-Resolution: A Unified Pipeline for Cancer Imaging\u201d poster<\/em><\/p>\n<\/div><\/div><\/div><\/div>

The second poster,\u00a0\u201cGenerating Lung CT Scans with Conditional Score-Based Diffusion Models\u201d<\/i>, presented a diffusion-based approach for generating realistic synthetic lung CT scan slices guided by anatomical segmentation masks. The work aims to address the scarcity of annotated medical imaging data while preserving anatomical coherence and privacy requirements.<\/p>\n<\/div>

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\u201cGenerating Lung CT Scans with Conditional Score-Based Diffusion Models\u201d <\/span>poster<\/em><\/p>\n<\/div><\/div><\/div><\/div>

These contributions reinforce INESC TEC\u2019s commitment to advancing AI-driven medical imaging research and supporting innovation in healthcare technologies.<\/p>\n<\/div><\/div><\/div>

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