---
license: mit
tags:
  - image-classification
  - biology
  - microscopy
  - resnet
datasets:
  - custom
metrics:
  - accuracy
  - f1
model-index:
  - name: resnet18
    results:
      - task:
          type: image-classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.965
          - name: F1
            type: f1
            value: 0.97
---

# resnet18

Binary classifier for detecting cancer cell presence on micropatterns in phase contrast microscopy images.

## Model description

Fine-tuned ResNet-18 (`microsoft/resnet-18`) that classifies 77x77 pixel grayscale crops as **present** (cancer cell on micropattern) or **absent** (empty micropattern). Part of the pipeline for analyzing T-cell killing assays.

## Training

- **Base model**: `microsoft/resnet-18`
- **Dataset**: 420 manually annotated samples (28 crops x 15 timepoints) from a single position (Pos150)
- **Class balance**: 60% present / 40% absent
- **Epochs**: 20
- **Batch size**: 32
- **Learning rate**: 1e-4
- **Validation split**: 20%
- **Training time**: ~45 seconds on Apple Silicon

## Performance

| Metric | Value |
|--------|-------|
| Accuracy | 96.5% |
| F1 Score | 0.97 |

### Control validation (Pos140, no T-cells added)

- 47/53 cells correctly classified as surviving all 50 timepoints
- 6 false deaths (~11% false positive death rate)

## Usage

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

processor = AutoImageProcessor.from_pretrained("keejkrej/resnet18")
model = AutoModelForImageClassification.from_pretrained("keejkrej/resnet18")

image = Image.open("crop.png").convert("RGB")
inputs = processor(image, return_tensors="pt")
outputs = model(**inputs)

predicted_class = outputs.logits.argmax(-1).item()
label = model.config.id2label[predicted_class]  # "present" or "absent"
```

## Input format

- 77x77 pixel grayscale microscopy crops (uint16 normalized to uint8)
- Converted to RGB (3-channel) before processing
- Single micropattern site per crop

## Labels

| ID | Label |
|----|-------|
| 0 | absent |
| 1 | present |