metadata
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
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 |