Multispectral texture analysis of histopathological abnormalities in colorectal tissues
Bibliographic record
Abstract
This paper proposes to use texture features extracted from multispectral microscopic images to detect histopathological abnormalities related to colorectal cancer (CRC): stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) and carcinoma (Ca). Texture features, based on gray-level co-occurrence matrices (GLCM) and discrete wavelets (DW), are obtained from colon biopsy images, captured using 16 different bands of the visible spectrum. A random forest classifier is used to evaluate the usefulness of these texture features, for each spectral band, on the task of discriminating between the four types of abnormal tissue. Preliminary results on the data of 39 CRC patients show that such features, in particular those based on GLCM and Symlet wavelets, can accurately predict the type of CRC tissue (94% accuracy, 88% sensibility and 100% specificity for Symlet features in the 16thspectral band). These results also reveal important differences in the textural information captured in each band, which could be used to develop more efficient procedures for the diagnosis of CRC.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".