FOCUS-7D_Public

Generalizability remains a significant challenge for artificial intelligence (AI) models in ultrasound imaging, primarily due to the scarcity of large-scale, diverse,and comprehensively annotated multi-organ data. To address this gap, we present FOCUS-7D (FOundational Cross-organ Ultrasound Set for 7 Diseases), a large-scale, multi-organ ultrasound dataset designed to foster the development of foundational AI models. This resource integrates 11,002 images (5,262 prospectively collected from four tertiary hospitals and 6,740 curated from public sources) across seven critical anatomical regions: thyroid, breast, liver, kidney, fetal head, heart,and appendix. The dataset supports both segmentation and classification tasks.All annotations underwent a rigorous multi-tier validation process, beginning with initial segmentation by junior annotators, followed by refinement under senior sonographer supervision, and concluding with final verification by physicians with over 10 years of experience. To demonstrate its utility, we established baseline bench?marks. Models trained on FOCUS-7D achieved high performance, with a notable mean Dice coefficient for segmentation and strong accuracy for classification, confirming the dataset’s quality and the robustness of the established benchmarks.By harmonizing diverse and clinically representative data, FOCUS-7D bridges the gap between AI research and real-world application, enabling the development of generalizable ultrasound AI models for enhanced diagnostic workflows.

The FOCUS-7D_Public dataset is collected from nine public datasets, and the list of these public datasets is as follows:
BUS-BRA: https://doi.org/10.5281/zenodo.8231412
BUSI: https://scholar.cu.edu.eg/?q=afahmy/pages/dataset
BUSIS: http://cvprip.cs.usu.edu/busbench
CAMUS: https://camus.creatis.insa-lyon.fr/challenge/
DDTI: https://drive.google.com/file/d/1wwlsEhwfSyvQsJBRjeDLhUjqZh8eaH2R/view
Fetal_HC: https://zenodo.org/records/1327317
KidneyUS: https://github.com/rsingla92/kidneyUS?tab=readme-ov-file
Appendix: http://dx.doi.org/10.5281/zenodo.7711412
Fatty-Liver: https://zenodo.org/records/1009146