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Training state-of-the-art pathology foundation models with orders of magnitude less data
We present novel pathology FMs trained with an adaptation of DINOv2 and post-training on higher-resolution images, achieving superior or comparable performance to state-of-the-art FMs while using up to two orders of magnitude fewer WSIs.
Mikhail Karasikov, Joost van Doorn, Nicolas Känzig, Melis Erdal Cesur, Hugo Mark Horlings, Robert Berke, Fei Tang, Sebastian Otálora
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Training state-of-the-art pathology foundation models with orders of magnitude less data
Indexing All Life's Known Biological Sequences
We demonstrate the feasibility of indexing the full extent of existing sequencing data and present new approaches for efficient and cost-effective full-text search.
Mikhail Karasikov, Harun Mustafa, Daniel Danciu, Marc Zimmermann, Christopher Barber, Gunnar Rätsch, André Kahles
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Indexing All Life's Known Biological Sequences
Towards large-scale training of pathology foundation models
We present and make publicly available the first batch of our pathology FMs trained on open-access TCGA whole slide images.
kaiko.ai, Nanne Aben, Edwin D. de Jong, Ioannis Gatopoulos, Nicolas Känzig, Mikhail Karasikov, Axel Lagré, Roman Moser, Joost van Doorn, Fei Tang
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Towards large-scale training of pathology foundation models
MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale
We present MetaGraph, a scalable framework for indexing large collections of sequencing data.
Mikhail Karasikov, Harun Mustafa, Daniel Danciu, Marc Zimmermann, Christopher Barber, Gunnar Rätsch, André Kahles
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MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale