Hyperspectral imaging for intraoperative diagnosis of colon cancer metastasis in a liver
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hyperspectral imaging (HSI) is being shown as an emerging modality with a great potential in disease diagnosis and surgical cancer resection. Herein, we evaluate feasibility of the HSI to discriminate and diagnose colon cancer metastasis in a liver from five hematoxylin and eosin stained histopathological specimens. They were collected from the same patient during intraoperative frozen section analysis. Cancer and non-cancer spectra along with corresponding spatial maps were estimated from hyperspectral images by means of spectral unmixing. It was found that maximal angle between cancer spectra is 1.02 degrees less than minimal angle between cancer vs. non-cancer spectra. Thus, spectrum angle mapper was used for pixel-based diagnosis of cancer yielding sensitivity between 81.23% and 97.12%, specificity between 85.85% and 97.3%, and accuracy between 86.85% and 96.92%.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it