Shypd.aiThe largest open index of AI toolspcam is an open-source deep learning classification benchmark dataset for medical imaging. It provides 327,680 color images of lymph node sections, each labeled for the presence of metastatic tissue, suitable for training models on a single GPU.
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The PatchCamelyon (PCam) benchmark is a challenging image classification dataset designed for deep learning in medical imaging. It comprises 327,680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annotated with a binary label indicating the presence of metastatic tissue, making it ideal for training and evaluating machine learning models for metastasis detection. PCam is larger than CIFAR10 but smaller than ImageNet, allowing models to be trained on a single GPU within a few hours. It serves as a valuable resource for fundamental machine learning research on topics such as active learning, model uncertainty, and explainability, particularly within the medical domain. The dataset is provided in gzipped HDF5 files and includes training, validation, and test sets with balanced positive and negative examples.
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