All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914709208301568 |
|---|---|
| author | Han, Changhee Shibano, Kyohei Ozaki, Wataru Osaki, Keishiro Haraguchi, Takafumi Hirahara, Daisuke Kimura, Shumon Kobayashi, Yasuyuki Mogi, Gento |
| author_facet | Han, Changhee Shibano, Kyohei Ozaki, Wataru Osaki, Keishiro Haraguchi, Takafumi Hirahara, Daisuke Kimura, Shumon Kobayashi, Yasuyuki Mogi, Gento |
| contents | Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global population share; and 2) hefty time and investment needed to curate proprietary datasets for commercial use. In response, we established the first commercial medical imaging platform, encompassing steps like: 1) data collection, 2) data selection, 3) annotation, and 4) pre-processing. Moreover, we focus on harnessing under-represented data from Japan and broader Asia, including Computed Tomography, Magnetic Resonance Imaging, and Whole Slide Imaging scans. Using the collected data, we are preparing/providing ready-to-use datasets for medical AI R&D by 1) offering these datasets to AI firms, biopharma, and medical device makers and 2) using them as training/test data to develop tailored AI solutions for such entities. We also aim to merge Blockchain for data security and plan to synthesize rare disease data via generative AI. DataHub Website: https://medical-datahub.ai/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06145 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing Han, Changhee Shibano, Kyohei Ozaki, Wataru Osaki, Keishiro Haraguchi, Takafumi Hirahara, Daisuke Kimura, Shumon Kobayashi, Yasuyuki Mogi, Gento Computer Vision and Pattern Recognition Artificial Intelligence Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global population share; and 2) hefty time and investment needed to curate proprietary datasets for commercial use. In response, we established the first commercial medical imaging platform, encompassing steps like: 1) data collection, 2) data selection, 3) annotation, and 4) pre-processing. Moreover, we focus on harnessing under-represented data from Japan and broader Asia, including Computed Tomography, Magnetic Resonance Imaging, and Whole Slide Imaging scans. Using the collected data, we are preparing/providing ready-to-use datasets for medical AI R&D by 1) offering these datasets to AI firms, biopharma, and medical device makers and 2) using them as training/test data to develop tailored AI solutions for such entities. We also aim to merge Blockchain for data security and plan to synthesize rare disease data via generative AI. DataHub Website: https://medical-datahub.ai/ |
| title | All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.06145 |