Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care
Bibliographic record
Abstract
Accurate interpretation of the hematoxylin and eosin (H&E) slide has remained the foundation of pathological analysis and diagnostic medicine for over a century. 1 For the pathologist, the H&E slide is equivalent to a high-quality patient history or physical exam. It combines art and science to help triage and guide more focused and specialized ancillary studies. Unfortunately, the perceived value of histomorphologic analysis in the era of precision medicine is diminishing in recent years due to the emergence of more contemporary and data-rich molecular studies. 2 , 3 , 4 Ironically, this is no different than the scrutiny that the patient history and physical exam have faced, in light of widely available whole-body imaging technologies. 5 , 6 , 7 Some have even proposed that given the exponential decrease in sequencing costs, medical assessment could effectively begin with whole-genome analysis. 8 Here, we discuss the current state and the possible future of the H&E stain by highlighting some of its strengths and shortcomings. It may well be that the scrutiny that the H&E microscopic exam has faced in recent years 4 is no fault of its own, but the lack of effective approaches to routinely extract more of the rich morphologic information it contains.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".