Multimodal Medical Engineering for Precision Medicine and Some Research Topics : 5월 12일 11:30~12:30 (DH 101)
Bibliographic record
Abstract
In this project, “Multimodal medical engineering,” we use several modalities such as CT, MRI and US to analyze the variation of cell or organ due to disease by engineering technologies and clarify its relationship. Based on the knowledge obtained there, we aim to establish some new methods for highly precise and non-invasive diagnosis and treatment. This project has an environment that engineering and medical researchers tightly collaborate and conduct a wide range of researches from fundamental study to preclinical and clinical test. Many state-of-the art equipment such CT, MRI, US, endoscope, microscope and apparatus for preparing pathological specimens are also available for promoting the project. In this project, we are also emphasizing the development of human resources through research activities. Students can receive education and advices from both medical and engineering professors. Thanks to active collaboration with companies, students can learn more practical mind in research and development. We also send students in the project to university or institute in foreign countries for long term or short term aiming the development of global human resources. Our project “Multimodal Medical Engineering for Precision Medicine” was also selected as a JSPS (Japan Society for the Promotion of Science) Core-to-Core program 2017 after very tough selection. The project lasts five years. International collaboration researches will be more activated by this support with (University of Eastern Finland (Finland), Thammasat University (Thailand), Shanghai Jiao Tong University (China) and University of Waterloo(Canada). In the presentation, some related research topics will be shortly introduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.181 | 0.071 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".