Multimodal analysis of whole slide images in colorectal cancer
Bibliographic record
Abstract
Multimodal models have enabled the integration of digital pathology, radiology, clinical information, and omics data to enhance Colorectal cancer (CRC) care. This systematic review critically appraises Multimodal digital pathology techniques applied in CRC, their performance, and contrasts them with foundation models. We identified and screened 1601 studies published between January 2014 and August 2024 using PubMed, Web of Science, Scopus, and IEEE Xplore (PROSPERO protocol: 635831). The quality and bias of the 22 eligible studies were assessed using the Newcastle-Ottawa Scale. Our findings suggest that majority of the studies integrated different modalities to enhance diagnostic accuracy and survival prediction. Various fusion techniques have been used to extract novel features. Most studies did not undertake external validation. Compared to unimodal models, multimodal approaches demonstrate superior performance but challenges remain, including constructing multimodal datasets, managing data heterogeneity, ensuring temporal alignment, determining modality weighting, and improving interpretability.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".