license: mit
pipeline_tag: image-classification
tags:
- medical
Model Details
This model is trained on 224X224 Grayscale images which were originally CT-scans that were transformed into JPG images. The model is a finetuned version of Swin Transformer (tiny-sized model). I also used this tutorial.Swin Transformer (tiny-sized model).
Uses
The model can be used to classify JPG images of CT scans into either cancer positive or Cancer negative groups. I think it would work okay for any image classification task.
Training Data
The model was trained on data originally obtained from the National Cancer Institute Imaging Data Commons. https://portal.imaging.datacommons.cancer.gov/explore/ The data set used consisted of about 11,000 images which were transformed CT scans some of which contained Cancerous Nodules and some that did not.
How to Use
Upload a grayscale JPG into the model inference section and it will cast a prediction. One comes included in this repo. If the image contains an X, it is a negative cancer image. If an image name contains a Y it is positive.
Results
{'eval_loss': 0.3047838807106018,
'eval_accuracy': 0.8452380952380952,
'eval_runtime': 4.4078,
'eval_samples_per_second': 209.627,
'eval_steps_per_second': 6.579,
'epoch': 6.975778546712803}