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All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing

arXiv 2403.06145 ↗First posted 10 Mar 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

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/

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 10 Mar 2024.

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References · 3

  1. 2020MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstructioncited by 50
  2. 2017Radiomics strategies for risk assessment of tumour failure in head-and-neck cancercited by 31
  3. 2020Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systemscited by 3

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