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--- |
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license: mit |
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pipeline_tag: image-classification |
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tags: |
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- medical |
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--- |
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## Model Purpose |
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Classify whether there is cancer or not in CT-scan images of the lungs. |
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## Model Details |
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This model is trained on 224X224 Grayscale images which were originally CT-scans |
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that were transformed into JPG images. The model is a finetuned version of |
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[Swin Transformer (tiny-sized model)](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224). |
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I also used this tutorial.[Swin Transformer (tiny-sized model)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb#scrollTo=UX6dwmT7GP91). |
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## Uses |
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The model can be used to classify JPG images of CT-scans into either cancer positive or |
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Cancer negative groups. |
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I think it would work okay for any image classification task. |
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## Training Data |
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The model was trained on data originally obtained from the National Cancer Institute |
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Imaging Data Commons. https://portal.imaging.datacommons.cancer.gov/explore/ |
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Specifically data from the National Lung Screening trial. |
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The data set used consisted of about 11,000 images which were transformed CT scans |
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some of which contained Cancerous Nodules and some that did not. |
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## How to Use |
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Upload a grayscale JPG into the model inference section and it will cast a prediction. |
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Some are included in this repo. If the image contains an X, it is a negative cancer image. |
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If an image name contains a Y it is positive. |
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## Results |
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``` |
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{'eval_loss': 0.3047838807106018, |
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'eval_accuracy': 0.8452380952380952, |
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'eval_runtime': 4.4078, |
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'eval_samples_per_second': 209.627, |
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'eval_steps_per_second': 6.579, |
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'epoch': 6.975778546712803} |
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