Semi-Supervised Tumor Response Grade Classification from Histology Images of Colorectal Liver Metastases
Why this work is in the frame
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Bibliographic record
Abstract
Colorectal liver metastases (CLM) develop in almost half of patients with colon cancer. Response to systemic chemotherapy is the main determinant of patient survival. Due to the importance of assessing treatment response of CLM to chemotherapy for the patient prognosis, there is a need to classify tumor response grade (TRG) on histopathology slides (HPS). However, annotating HPS for training neural networks is a time-consuming task. In this work, we present an end-to-end approach for tissue classification of CLM slides leading to TRG prediction. A weakly-supervised model is first trained to perform tissue classification from sparse annotations, generating segmentation maps. Then, using features extracted for these maps, a secondary model is trained to perform the TRG classification. We demonstrate the feasibility of the proposed approach on a clinical dataset of 1450 HPS from 232 CLM patients by comparing our semi-supervised Mean Teacher approach with other supervised and semi-supervised methods. The proposed pipeline outperforms other models, achieving a classification accuracy of 94.4%. Based on the generated classification maps, the model is able to stratify patients into two TRG classes (1-2 vs 3-5) with an accuracy of 86.2%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it