Histological Image Classification using Deep Features and Transfer Learning
Bibliographic record
Abstract
A major challenge in the automatic classification of histopathological images is the limited amount of data available. Supervised learning techniques cannot be applied without some adjustment. We compare two common techniques to deal with limited domain data: using deep features and fine-tuning convolutional neural networks (CNN). We examine the following state-of-art CNN models: SqueezeNet-v1.1, MobileNet-v2, ResNet-18, and DenseNet-201. We demonstrate that using feature vectors that are extracted from one of the four CNN models with a classical support vector machine (SVM) for training and testing can lead to higher accuracy on publicly available datasets: Warwick-QU, Epistroma, BreaKHis, multi-class Kather, than previously published results. Similar results can be obtained with fully fine-tuning the aforementioned CNN models. We also study the effectiveness of block-wise fine-tuning of two models (i.e., SqueezeNet-v1.1 and ResNet-18) and show that it is not necessary to fully fine-tune leading to savings in time and space.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".