專案影像-X：針對基礎模型開發的1000多個開放取用醫學影像資料集調查研究

摘要

基礎模型憑藉大規模、多樣化且高品質資料集的蓬勃發展，在多個領域和任務中展現出卓越成效。然而在醫學影像領域，由於對臨床專業知識的依賴以及嚴格的倫理與隱私限制，此類醫學資料集的整理與匯集面臨巨大挑戰，導致大規模統一醫學資料集的匱乏，阻礙了強大醫學基礎模型的發展。本研究提出迄今規模最全面的醫學影像資料集調研，系統性歸納超過1,000個開放取用資料集，涵蓋其影像模態、任務類型、解剖部位、標註類型、局限性及整合潛力。分析顯示現有醫學影像資料集呈現規模有限、任務範疇碎片化、器官與模態分佈不均等特徵，這制約了其對開發通用魯棒醫學基礎模型的支撐作用。為將碎片化資源轉化為規模化優勢，我們提出元資料驅動的融合範式（MDFP），通過整合具有共享模態或任務的公共資料集，將多個小型資料孤島轉化為更龐大連貫的資源。基於MDFP框架，我們發布互動式探索平台，實現端到端自動化醫學影像資料集整合，並將所有調研資料集編纂為結構化表格，清晰概括關鍵特徵並提供參考鏈接，為學界提供易於使用的綜合資源庫。通過繪製現狀圖譜並提出資料集整合的系統方法，本研究為擴展醫學影像語料庫提供了實踐路線圖，助力加速資料發現、推動更規範的資料集構建，並促進高性能醫學基礎模型的發展。

English

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical expertise and strict ethical and privacy constraints, resulting in a scarcity of large-scale unified medical datasets and hindering the development of powerful medical foundation models. In this work, we present the largest survey to date of medical image datasets, covering over 1,000 open-access datasets with a systematic catalog of their modalities, tasks, anatomies, annotations, limitations, and potential for integration. Our analysis exposes a landscape that is modest in scale, fragmented across narrowly scoped tasks, and unevenly distributed across organs and modalities, which in turn limits the utility of existing medical image datasets for developing versatile and robust medical foundation models. To turn fragmentation into scale, we propose a metadata-driven fusion paradigm (MDFP) that integrates public datasets with shared modalities or tasks, thereby transforming multiple small data silos into larger, more coherent resources. Building on MDFP, we release an interactive discovery portal that enables end-to-end, automated medical image dataset integration, and compile all surveyed datasets into a unified, structured table that clearly summarizes their key characteristics and provides reference links, offering the community an accessible and comprehensive repository. By charting the current terrain and offering a principled path to dataset consolidation, our survey provides a practical roadmap for scaling medical imaging corpora, supporting faster data discovery, more principled dataset creation, and more capable medical foundation models.

專案影像-X：針對基礎模型開發的1000多個開放取用醫學影像資料集調查研究

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

摘要

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