HEp-2 Cell Classification Using an Ensemble of Convolutional Neural Networks
Bibliographic record
Abstract
The presence of Anti-nuclear Autoantibodies (ANA) in human serum is related to autoimmune diseases. Indirect Immunofluorescence (IIF) imaging on human epithelial type-2 cells (HEp-2) is the gold standard for the ANA test. Accurate Human Epithelial-2 (HEp-2) cell classification plays an essential role in the diagnosis of immune system diseases. Developing computer-aided diagnosis (CAD) systems for ANA analysis is necessary for improving the disease diagnosis and treatment planning of the patients. Traditionally, cell patterns are assessed manually with a fluorescence microscope. However, due to the large variations of cell patterns, manual interpretation of the cell images is time-consuming, highly subjective and also requires experienced experts. In this paper, we propose a deep learning-based ensemble model of Inception V3and Xception architectures for the task of HEp-2 cell images classification as either healthy or case subjects. In intuition, the aggregation of different architectures can effectively extract and fuse the most discriminative deep features from input images. Also, we use a transfer learning strategy and hyper-parameter tuning to further improve the performance of the proposed model. Experimental results demonstrate that our proposed ensemble model achieves promising results with an accuracy of 95.07%, sensitivity of 99.96% and specificity of 99.79% in comparison with state-of-the-art models on the ICPR 2012 benchmark dataset.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".