Models#
List all available models. |
Vision models#
GitHub Paper Params: 27.5M 768 features AGPL-3.0 [Wang et al., 2024] Clinical Histopathology Imaging Evaluation Foundation (CHIEF) |
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🤗Hugging Face GitHub Paper Params: 395.2M 512 features CC-BY-NC-ND-4.0 [Lu et al., 2024] CONtrastive learning from Captions for Histopathology (CONCH) |
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GitHub Paper Params: 27.5M 768 features GPL-3.0 [Wang et al., 2022] Transformer-based unsupervised contrastive learning for histopathological image classification |
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🤗Hugging Face GitHub Paper Params: 1.13B 1536 features Apache 2.0 with conditions [Xu et al., 2024] A whole-slide foundation model for digital pathology |
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🤗Hugging Face GitHub Paper Params: 85.7M 1536 features CC-BY-NC-ND-4.0 [Filiot et al., 2025] A distilled version of H-optimus-0 |
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🤗Hugging Face GitHub Params: 1.13B 1536 features Apache 2.0 [Saillard et al., 2024] Vision foundation model |
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🤗Hugging Face GitHub Params: 1.13B 1536 features CC-BY-NC-ND-4.0 [Bioptimus, 2025] Vision foundation model |
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🤗Hugging Face GitHub Paper Params: 85.7M 768 features Apache 2.0 [Nechaev et al., 2024] A family of foundational vision transformers for pathology |
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🤗Hugging Face GitHub Paper Params: 303.7M 1024 features Apache 2.0 [Nechaev et al., 2024] A family of foundational vision transformers for pathology |
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🤗Hugging Face GitHub Paper Params: 1.14B 3072 features MIT [Karasikov et al., 2025] Training state-of-the-art pathology foundation models with orders of magnitude less data |
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🤗Hugging Face GitHub Paper Params: 87.8M 512 features Non-commercial [Huang et al., 2023] Pathology Language-Image Pretraining (PLIP) |
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🤗Hugging Face GitHub Paper Params: 85.8M 768 features Owkin non-commercial license [Filiot et al., 2023] Scaling self-Supervised Learning for histopathology with Masked Image Modeling |
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🤗Hugging Face GitHub Paper Params: 303.4M 1024 features Owkin non-commercial license [Filiot et al., 2024] A large and public feature extractor for biomarker prediction |
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🤗Hugging Face GitHub Paper Params: 303.4M 1024 features CC-BY-NC-ND-4.0 [Chen et al., 2024] General-purpose self-supervised model for pathology |
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🤗Hugging Face GitHub Paper Params: 681.4M 1536 features CC-BY-NC-ND-4.0 [Chen et al., 2024] An improved version of UNI |
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🤗Hugging Face Paper Params: 631.2M 2560 features Apache 2.0 [Vorontsov et al., 2024] A foundation model for clinical-grade computational pathology and rare cancers detection |
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🤗Hugging Face Paper Params: 631.2M 2560 features CC-BY-NC-ND-4.0 [Zimmermann et al., 2024] Scaling self-supervised mixed magnification models in pathology |
Multimodal models#
🤗Hugging Face GitHub Paper Params: 395.2M 512 features CC-BY-NC-ND-4.0 [Lu et al., 2024] CONtrastive learning from Captions for Histopathology (CONCH) |
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🤗Hugging Face GitHub Paper Params: 638.5M BSD-3-Clause [Chen et al., 2025] A visual-omics foundation model to bridge histopathology with spatial transcriptomics |
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🤗Hugging Face GitHub Paper Params: 87.8M 512 features Non-commercial [Huang et al., 2023] Pathology Language-Image Pretraining (PLIP) |
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🤗Hugging Face Paper Params: 557.7M CC-BY-NC-ND-4.0 [Shaikovski et al., 2024] A multi-modal generative foundation model for slide-level histopathology, the Prism models encode slide-level embeddings from |
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🤗Hugging Face GitHub Paper Params: 158.9M 768 features CC-BY-NC-ND-4.0 [Ding et al., 2024] Multimodal whole slide foundation model for pathology |
Segmentation models#
🤗Hugging Face GitHub Paper BSD-3-Clause [Stringer et al., 2021] Cell segmentation model |
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GitHub Paper Params: 6.3M CC-BY-NC-SA-4.0 [Weng et al., 2024] Artifact segmentation model from GrandQC |
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GitHub Paper Params: 6.6M CC-BY-NC-SA-4.0 [Weng et al., 2024] Tissue segmentation model from GrandQC |
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Apply the InstaSeg model to the input image.:octicon:check-circle-fill;1em;sd-text-success; GitHub Paper Params: 3.8M Apache 2.0 [Goldsborough et al., 2024] An embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation. |
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GitHub Paper Params: 47.9M Apache 2.0; CC-BY-NC-SA-4.0 [Tommasino et al., 2024] Nuclei instance segmentation and classification |
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Tissue segmentation model from PathProfiler. |
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This is a base class for any models from segmentation models pytorch |
Tile prediction models#
GitHub Paper Params: 299 Prosperity Public License 3.0.0 [Wang et al., 2020] High efficiency Focus Quality Assessment for digital pathology |
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GitHub Paper Params: 11.2M GPL-3.0 [Haghighat et al., 2022] Quality assessment of histology images |
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🤗Hugging Face Params: 303.9M CC BY-NC 4.0 Tile classification for breast |
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🤗Hugging Face Params: 303.9M CC BY-NC 4.0 Tile classification for colorectal |
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🤗Hugging Face Params: 303.9M CC BY-NC 4.0 Tile classification for skin |
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🤗Hugging Face Params: 303.9M CC BY-NC 4.0 Tile classification for thorax |
Slide encoder models#
🤗Hugging Face Paper Params: 557.7M CC-BY-NC-ND-4.0 [Shaikovski et al., 2024] A multi-modal generative foundation model for slide-level histopathology, the Prism models encode slide-level embeddings from |
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🤗Hugging Face GitHub Paper Params: 158.9M 768 features CC-BY-NC-ND-4.0 [Ding et al., 2024] Multimodal whole slide foundation model for pathology |
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GitHub Paper Params: 1.2M AGPL-3.0 [Wang et al., 2024] Clinical Histopathology Imaging Evaluation Foundation (CHIEF) |
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🤗Hugging Face GitHub Paper Apache 2.0 with conditions [Xu et al., 2024] A whole-slide foundation model for digital pathology |
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🤗Hugging Face GitHub Paper Params: 3.2M CC BY-NC-ND 4.0 [Jaume et al., 2024] Multistain Pretraining for Slide Representation Learning in Pathology |
Computer vision features#
Calculate the brightness of a tile. |
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Calculate the canny edge detection score of a tile. |
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Calculate the contrast of a tile. |
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Calculate the entropy of a tile. |
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Calculate texture features using Gray Level Co-occurrence Matrix (GLCM). |
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Calculate the color saturation of a tile. |
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Calculate the sharpness of a tile. |
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Calculate the sobel of a tile. |
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Calculate the RGB value of a tile. |