A Bladder Cancer Grading System Using Deep Neural Network Architectures
Bibliographic record
Abstract
Histopathologists grade bladder cancer by looking at histology images or glass-slides of bladder tissues. Grading is an important stage of bladder cancer diagnosis and key requirement to determine the proper course of treatment. This work presents a machine-learning-based platform, known as Bladder Cancer Grading (BCG) system, a platform designed and developed based on deep neural network architectures. The main input to BCG is a high resolution image of bladder tissue scanned from glass slide, known as Whole Slide Image (WSI). In learning stage, BCG breaks a set of sample WSI images into equally-sized square tiles in order to build its learning dataset. In projection stage, similar to learning stage, BCG breaks down any given input WSI image into tiles and projects every single tile using the selected trained model. BCG uses a 4-tier grading scheme to grade each tile. The grading scheme is derived from World Health Organization (WHO) 1973/2004 bladder-cancer-grading-schemes [1] that are globally accepted and practiced by many specialists including pathologists working in University Health Network(UHN) in Toronto General 1 and Mount Sinai Hospitals 2. Using its distinct tiling design approach, BCG has introduced a grading scheme that projects a decimal grading value per each slide unlike existing practice that assigns a discrete grade value between 1 and 4 to each slide. The team of senior pathologists who assisted labeling BCG training dataset believe this new grading approach provides a better state of progression in bladder cancer resulting in more precise diagnosis and better treatment procedures. The choice of learning model(s) in BCG is configurable. Any deep architecture model could be plugged in, trained, and used by BCG. Some trained models developed by this platform have shown promising grading accuracy (more than 97% in verification/testing, above 85% in real projection, and 97+ specificity rate). BCG has also shown a highly consistent intro-observatory results. The combination of a loosely coupled architecture and fully integrated utilization of tiles in all stages of its execution have made BCG a universal, expandable, scalable, and versatile platform that could be configured and deployed in distributed running environments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.002 |
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".