ChiShengChen/pytorch_deep_learning_medimg_tutorial
This project simply implements three common deep learning models (VGG16, ResNet18, ViT) for medical image classification tasks using the PathMNIST dataset.
This project helps medical professionals, researchers, or students in pathology classify medical images. It takes raw pathology images (specifically 28x28 pixel RGB images of tissue types) and categorizes them into 9 distinct tissue types like adenocarcinoma, lymphoma, or tumor epithelium. This tool is designed for anyone needing to understand or apply deep learning models for medical image classification.
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Use this if you want to experiment with or learn how common deep learning models (VGG16, ResNet18, ViT) are implemented and perform on a standard medical image classification task.
Not ideal if you need to classify complex, high-resolution medical images from a new dataset, or if you require a production-ready, highly optimized classification system without deep learning knowledge.
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Apr 14, 2025
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