Instructions to use SerdarHelli/ThyroidTumorClassificationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SerdarHelli/ThyroidTumorClassificationModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SerdarHelli/ThyroidTumorClassificationModel") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("SerdarHelli/ThyroidTumorClassificationModel") model = AutoModelForImageClassification.from_pretrained("SerdarHelli/ThyroidTumorClassificationModel") - Notebooks
- Google Colab
- Kaggle
Commit ·
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README.md
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@@ -12,4 +12,9 @@ Thyroid nodule is one of the most common endocrine carcinomas. Due to its higher
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In this study, the purpose is the classification of thyroid tumors on ultrasound images with 2 different categories:
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- Malign(1)
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- Benign(0)
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In this study, the purpose is the classification of thyroid tumors on ultrasound images with 2 different categories:
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- Malign(1)
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- Benign(0)
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The Dataset:
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[Colombia National University presented an open access database of thyroid ultrasound images.](http://cimalab.unal.edu.co/?lang=es&mod=program&id=5)
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Ref : Pedraza, Lina & Vargas, Carlos & Narváez, Fabián & Durán, Oscar & Muñoz, Emma & Romero, Eduardo. (2015). An open access thyroid ultrasound-image Database. Progress in Biomedical Optics and Imaging — Proceedings of SPIE. 9287. 10.1117/12.2073532.
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