Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging
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
Colorectal cancer (CRC) remains a leading global health challenge, with early and accurate diagnosis crucial for effective treatment. Histopathological evaluation, the current diagnostic gold standard, faces limitations including subjectivity, delayed results, and reliance on well-prepared tissue slides. Mass spectrometry imaging (MSI) offers a complementary approach by providing molecular-level information, but its high dimensionality and the scarcity of labeled data present unique challenges for traditional supervised learning. In this study, we present the first implementation of foundation models for MSI-based cancer classification using desorption electrospray ionization (DESI) data. We evaluate multiple architectures adapted from other domains, including a spectral classification model known as FACT, which leverages audio–language pretraining. Compared to conventional machine learning approaches, these foundation models achieved superior performance, with FACT achieving the highest cross-validated balanced accuracy (93.27%±3.25%) and AUROC (98.4%±0.7%). Ablation studies demonstrate that these models retain strong performance even under reduced data conditions, highlighting their potential for generalizable and scalable MSI-based cancer diagnostics. Future work will explore the integration of spatial and multi-modal data to enhance clinical utility.
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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