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2026
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AACR
H-Optimus-1: A foundation model for computational histopathology

Pathology AI usually needs a different model for every task. H-Optimus-1 is one model with 1.1B parameters, trained on one of the largest histology datasets. It is hitting state-of-the-art across major external benchmarks.

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2026
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AACR
A practical guideline for benchmarking unimodal and multimodal foundation models on spatial transcriptomics data in oncology

The benchmarks judging foundation models for spatial omics are too coarse to trust. This infographic poster lays out how to benchmark with transparent baselines, diverse datasets, biologically meaningful tasks, and new oncology-specific metrics. A practical standard for evaluating the next generation of models, presented at the American Association for Cancer Research Annual Meeting 2026.

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2026
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AACR
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Anna C. Wahl-Schaar, Dasha Valter, Lisa Sikkema, Stefan G. Stark, Ediem Al-Jibury, Tobias Heinen, Liam Gonzalez, Marin Scalbert, Tokuwa Kanno, Zelda Mariet +
A practical guideline for benchmarking unimodal and multimodal foundation models on spatial transcriptomics data in oncology
The benchmarks judging foundation models for spatial omics are too coarse to trust. This infographic poster lays out how to benchmark with transparent baselines, diverse datasets, biologically meaningful tasks, and new oncology-specific metrics. A practical standard for evaluating the next generation of models, presented at the American Association for Cancer Research Annual Meeting 2026.
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Topics:
Bioptimus Research
2026
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AACR
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Marin Scalbert, Charlie Saillard, Thomas Peeters, Liam Gonzalez, Dasha Valter, Felipe Llinares-López, Zelda Mariet, Rodolphe Jenatton
H-Optimus-1: A foundation model for computational histopathology
Pathology AI usually needs a different model for every task. H-Optimus-1 is one model with 1.1B parameters, trained on one of the largest histology datasets. It is hitting state-of-the-art across major external benchmarks.
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Model:
H-Optimus
Topics:
Bioptimus Research
2024
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GitHub / bioRxiv
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Saillard C, Jenatton R, Llinares-López F, Mariet Z, Cahané D, Durand E, Vert JP (Bioptimus)
H-optimus-0: A Foundation Model for Computational Pathology
H-optimus-0 is Bioptimus's inaugural open-source pathology foundation model — a 1.1B-parameter ViT-g/14 trained with self-supervised learning on over 500,000 whole-slide images from 4,000+ clinical centers worldwide. The model achieves state-of-the-art performance across tile- and slide-level benchmarks including cancer subtyping, biomarker prediction, and gene expression estimation. As the foundational release of Bioptimus's model family, H-optimus-0 has exceeded one million downloads and is widely adopted by pharmaceutical and academic researchers.
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Model:
H-Optimus
Topics:
Bioptimus Research
Foundation Models for Computational Pathology
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