Cell Phenotype Classification Using Deep Residual Network and Its Variants
Bibliographic record
Abstract
Deep residual network (ResNet) is currently the basis of many popular state-of-the-art convolutional neural network models for image recognition, and its recent variants include wide residual network (WRN), aggregated deep residual network (ResNeXt) and deep pyramidal residual network (PyramidNet). Here, we demonstrate the potential application of deep residual network and its variants in high-content screening (i.e. cell phenotype classification) that can overcome issues associated with analyzing high-content screening data, such as exhaustive preprocessing and inefficient learning. Cell phenotype classification is an image-based method that can be used for drug high-content screening, in which complex cell states associated with chemical compound treatment can be characterized. Previous work on cell phenotype classification typically requires a routine yet cumbersome step of single cell segmentation before the classification task. In this paper, we present a segmentation-free method for image-based cell phenotype classification using deep ResNet and its variants. The cell images are samples treated with annotated compounds that can be mainly grouped into three clusters, giving three classes to be classified. Instead of single-cell phenotype classification, we use the raw images without segmentation for our training and evaluation directly. Compared to previous reference work, we significantly simplify the data preprocessing step and accelerate the training while still achieving high accuracy. Our trained models achieve a 98.2% accuracy rate on the three classes classification problem (three compound clusters only), and a 93.8% accuracy rate on the four classes classification problem (three compound clusters plus the mock class) based on five-fold cross-validation.
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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 | 0.000 |
| 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".