Multi-Level Batch Normalization in Deep Networks for Invasive Ductal Carcinoma Cell Discrimination in Histopathology Images
Bibliographic record
Abstract
Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide. Imaging techniques such as breast cancer pathology helps in the diagnosis and monitoring of the disease. However identification of malignant cells can be challenging given the high heterogeneity in tissue absorption from staining agents. In this work, we present a novel approach for Invasive Ductal Carcinoma (IDC) cells discrimination in histopathology slides. We propose a model derived from the Inception architecture, proposing a multi-level batch normalization module between each convolutional steps. This module was used as a base block for feature extraction in a CNN architecture. We used the open IDC dataset in which we obtained a balanced accuracy of 0.89 and an F1 score of 0.90, thus surpassing recent state of the art classification algorithms tested on this public dataset.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".