Deep Learning Based Intelligent Tumor Analytics Framework for Quantitative Grading and Analyzing Cancer Metastasis: Case of Lymph Node Breast Cancer
Bibliographic record
Abstract
False-positive or false-negative detection, and the resulting inappropriate treatments in cancer metastasis cases, have led to numerous fatal instances due to human errors. Traditional cancer diagnoses are often subjectively interpreted through naked-eye observation, which can vary among different medical practitioners. In this research, we propose a novel deep learning-based framework called Intelligent Tumor Analytics (ITA). ITA facilitates on-the-fly assessment of Whole Slide Imaging (WSI) at the histopathological level, primarily utilizing cellular appearance, spatial arrangement, and the relative proximities of various cell types (e.g., tumor cells, immune cells, and other objects of interest) observed within scanned WSI images of tumors. By automatically quantifying relevant indicators and estimating their scores, ITA establishes a standardized evaluation that aligns with widely recognized international tumor grading standards, including the TNM and Nottingham Grading Standards. The objective measurements and assessments offered by ITA provide informative and unbiased insights to users (i.e., pathologists) involved in determining prognosis and treatment plans. The quantified information regarding tumor risk and potential for further metastasis possibilities serves as crucial early knowledge during cancer development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".