Harvard Researchers Introduce A Novel ViT Architecture Called Hierarchical Image Pyramid Transformer (HIPT) That Can Scale Vision Transformers To Gigapixel Images Via Hierarchical Self-Supervised Learning

🚦 HIPT is pretrained across 33 cancer types using 10,678 gigapixel WSIs, 408,218 4096×4096 images, and 104M 256 × 256 images

🚦 HIPT pushes the boundaries of both Vision Transformers and self-supervised learning in two important ways.

🚦 The code is available

[Continue reading](https://www.marktechpost.com/2022/06/18/harvard-researchers-introduce-a-novel-vit-architecture-called-hierarchical-image-pyramid-transformer-hipt-that-can-scale-vision-transformers-to-gigapixel-images-via-hierarchical-self-supervised-lear/) | *Checkout the* [*paper*](https://arxiv.org/pdf/2206.02647.pdf)*,* [*github*](https://github.com/mahmoodlab/HIPT)

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https://i.redd.it/wrm3nvnceg691.gif

architecture harvard hierarchical image images learning machinelearningnews researchers scale self-supervised learning supervised learning transformer transformers vision