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Gento Mogi

Publications and source records attributed to Gento Mogi.

3 recordsLinked to original sources

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

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/

cs.CV

Wood traceability system using blockchain and zero-knowledge proof

The system proposed in this study uses zero-knowledge proof (ZKP) to verify the traceability of wood recorded in a public blockchain. Wood is a byproduct of several states, ranging from standing trees to logs, lumber, and wood products (hereinafter ``wood objects''). The advantage of using the blockchain for record keeping is that participants can freely record the information at their discretion, without any restrictions. However, the openness of the blockchain may allow a malicious third party to introduce disinformation. In this study, we employ ZKP and near-field communication (NFC) chips to eliminate the possibility of disinformation introduction. ZKP is used to prove/validate changes in the state of wood objects, and the unique nonce associated with that state is encrypted and recorded on an NFC chip. The nonce is concealed and id of the wood object is defined as hash value of this nonce. We developed a prototype system based on an Android application and an Ethereum smart contract. We confirm that wood traceability and verification can be performed using the prototype system.

cs.CR

Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical Investigation

Our study empirically predicts the bubble of non-fungible tokens (NFTs): transferable and unique digital assets on public blockchains. This topic is important because, despite their strong market growth in 2021, NFTs on a project basis have not been investigated in terms of bubble prediction. Specifically, we applied the logarithmic periodic power law (LPPL) model to time-series price data associated with four major NFT projects. The results indicate that, as of December 20, 2021, (i) NFTs, in general, are in a small bubble (a price decline is predicted), (ii) the Decentraland project is in a medium bubble (a price decline is predicted), and (iii) the Ethereum Name Service and ArtBlocks projects are in a small negative bubble (a price increase is predicted). A future work will involve a prediction refinement considering the heterogeneity of NFTs, comparison with other methods, and the use of more enriched data.

q-fin.ST