Pathology Feature Extractors and Foundation Models

We are witnessing the emergence of many new feature extractors trained using self-supervised learning on large pathology datasets. This repository aims to provide a comprehensive list of these models, alongside key information about them.

I aim to update this list as new models are released, but please submit a pull request / issue for any models I have missed!

Patch-level models

Name	Group	Weights	Released	SSL	WSIs	Tiles	Patients	Batch size	Iterations	Architecture	Parameters	Embed dim	Input size	Magnification	Dataset	Links
CTransPath	Sichuan University / Tencent AI Lab	✅	Dec 2021*	SRCL	32K	16M				Swin-Transformer	28M	768	224	~4-10x**	TCGA, PAIP
RetCCL	Sichuan University / Tencent AI Lab	✅	Dec 2021*	CCL	32K	16M	11K	2048	100 epochs	ResNet-50	26M	2048	224	~4-10x**	TCGA, PAIP
REMEDIS	Google Research	✅	May 2022*	SimCLR/BiT	29K	50M	11K cases	4096	1.2M	ResNet-50	26M	2048	224	multi-scale	TCGA
HIPT	Mahmood Lab	✅	Jun 2022*	DINOv1	11K	100M		256	400K	ViT-S	22M	384	256	~18-28x**	TCGA
Lunit-DINO	Lunit	✅	Dec 2022*	DINOv1	21K					ViT-S	22M	384	224	~9-28x**	TCGA
Lunit-{BT,MoCoV2,SwAV}	Lunit	✅	Dec 2022*	{BT,MoCoV2,SwAV}	21K					ResNet-50		2048	224	~9-62x**	TCGA
Phikon	Owkin	✅	Jul 2023*	iBOT	6.1K	43M	5.6K	1440	155K	ViT-B	86M	768	224	~20-35x**	TCGA
CONCH (VL)	Mahmood Lab	✅	Jul 2023*	iBOT & vision-language pretraining	21K	16M		1024	80 epochs	ViT-B	86M	768	224		proprietary
UNI	Mahmood Lab	✅	Aug 2023*	DINOv2	100K	100M				ViT-L		1024	224	~9-25x**	proprietary (Mass-100K)
Virchow	Paige / Microsoft	✅	Sep 2023*	DINOv2	1.5M		120K			ViT-H	632M	2560	224	~20-35x**	proprietary (from MSKCC)
Campanella et al. (DINO)	Thomas Fuchs Lab	✅	Oct 2023*	DINOv1	420K	3.3B	77K	1080	1.3K INE	ViT-S	22M	384	224	~20-32x**	proprietary (MSHS)	()
Campanella et al. (MAE)	Thomas Fuchs Lab	❌	Oct 2023*	MAE	420K	3.3B	77K	1440	2.5K INE	ViT-L	303M	1024	224	~20-45x**	proprietary (MSHS)
Path Foundation	Google	✅	Oct 2023*	SimCLR, MSN	6K	60M		1024		ViT-S	22M	384	224	~2-32x**	TCGA
PathoDuet	Shanghai Jiao Tong University	✅	Dec 2023*	inspired by MoCoV3	11K	13M		2048	100 epochs	ViT-B		4096	224	~18-124x**	TCGA
RudolfV	Aignostics	❌	Jan 2024*	DINOv2	130K	750M	36K			ViT-L	300M		224	~18-31x**	proprietary (from EU & US), TCGA
kaiko	kaiko.ai	✅	Mar 2024*	DINOv2	29K	260M**		512	200 INE	ViT-L		1024	224	~4-62x**	TCGA
PLUTO	PathAI	❌	May 2024*	DINOv2 (+ MAE and Fourier loss)	160K	200M				FlexiViT-S	22M		224	~5-71x**	proprietary (PathAI)
BEPH	Shanghai Jiao Tong University	✅	May 2024*	BEiTv2	12K	12M		1024		ViT-B	193M	1024	224	~40-89x**	TCGA
Prov-GigaPath	Microsoft / Providence	✅	May 2024*	DINOv2	170K	1.4B	30K	384		ViT		1536	224	~18-31x**	proprietary (Providence)
Hibou-B	HistAI	✅	Jun 2024*	DINOv2	1.1M	510M	310K cases	1024	500K	ViT-B	86M	768	224	~20-35x**	proprietary
Hibou-L	HistAI	✅	Jun 2024*	DINOv2	1.1M	1.2B	310K cases	1024	1.2M	ViT-L	304M	1024	224	~20-35x**	proprietary
H-optimus-0	Bioptimus	✅	Jul 2024*	DINOv2/iBOT	500K (across 4,000 clinics)	>100M	200K			ViT-G with 4 registers	1.1B	1536	224	~20-35x**	proprietary
mSTAR (VL)	Smart Lab	❌	Jul 2024*	mSTAR (multimodal)	10K		10K			ViT-L			224		TCGA
GPFM	Smart Lab	✅	Jul 2024*	DINOv2 (+ expert KD)	72K	190M		1536	500K	ViT-L	307M	1024	224	~9-31x**	TCGA, GTEx, CPTAC, + 30 public datasets
Virchow 2	Paige / Microsoft	✅	Aug 2024*	DINOv2 (+ ECT and KDE)	3.1M	2B	230K	4096		ViT-H with 4 registers	632M	3584	224	~5-42x**	proprietary (from MSKCC and international sites)
Virchow 2G	Paige / Microsoft	❌	Aug 2024*	DINOv2 (+ ECT and KDE)	3.1M	2B	230K	3072		ViT-G with 8 registers	1.9B	3584	224	~5-42x**	proprietary (from MSKCC and international sites)
Phikon-v2	Owkin	✅	Sep 2024*	DINOv2	58.4K	456M		4096	250K	ViT-L	307M	1024	224	~20-35x**	PANCAN-XL (TCGA, CPTAC, GTEx, proprietary)
CONCH1.5a (VL)	Mahmood Lab	✅	Nov 2024*	CoCa (vision-language), UNI-initialized		1.3M		256	20 epochs	ViT-L/16	306M**	768	448		proprietary
MUSKV (VL)	Li Lab (Stanford)	✅	Jan 2025*	Unified masked modeling (MLM, MIM) + contrastive learning	33K	50M	12K	2048	20 epochs	BEiT3			384	~10-40x**	TCGA
Atlas	Mayo, Charité, Aignostics	❌	Jan 2025*		1.2M	3.4B	490K cases			ViT-H	632M			~4-62x**
UNI2-h	Mahmood Lab	✅	Jan 2025*	DINOv2	350K	200M				ViT-H with 8 registers	681M	1536	224	~9-25x**	proprietary (Mass)
UNI2-g-preview	Mahmood Lab	❌	Jan 2025*	DINOv2	350K	200M				ViT-G				~9-25x**	proprietary (Mass)
PathOrchestra	Shanghai AI Lab	✅	Mar 2025*	DINOv2	290K	140M	41K cases	3072	20 epochs	ViT-L	304M	1024	224	~18-25x**	proprietary, TCGA
H-optimus-1	Bioptimus	✅	Apr 2025*		1M+ (across >4K clinics)		800K			ViT-g/14	1.1B	1536	224		proprietary

Notes:

• Models marked with VL indicate language-vision pretraining (others are vision-only)
• Models trained on >100K slides may be considered foundation models and are marked in bold
• # of WSIs, tiles, and patients are reported to 2 significant figures
• INE = ImageNet epochs
• Order is chronological
• Some of these feature extractors have been evaluated in a benchmarking study for whole slide classification here.
• ** means inferred from other numbers provided in the paper, repository, or elsewhere
• ^a CONCH1.5 was released as the patch encoder for TITAN, which is a slide-level foundation model. It is also the patch encoder of THREADS.
• Input size refers to the size of the input image at inference time (in pixels)
• Magnification is the effective magnification at which the model sees tissue during pretraining, accounting for any resizing, cropping, or other augmentations. For example, if patches are obtained at 20x with patch size 1024 but resized to 224 before being fed to the model, the effective magnification is 20x × (224/1024) ≈ 4.4x. We assume the following MPP-to-magnification relationship: 0.5 MPP = 20x, 1 MPP = 10x (i.e., MPP × magnification = 10). Often times, models may use random crops; in this case we estimate based on the parameters of the random crop augmentation.

Slide-level / patient-level models

This table includes models that produce slide-level or patient-level embeddings without supervision.

Name	Group	Weights	Released	SSL	WSIs	Patients	Batch size	Iterations	Architecture	Parameters	Embed dim	Patch size	Dataset	Links
GigaSSL	CBIO	✅	Dec 2022*	SimCLR	12K			1K epochs	ResNet-18		256	256	TCGA
PRISM (VL)	Paige / Microsoft	✅	May 2024*	contrastive (with language)	590K (190K text reports)	190K	64 (x4)	75K (10 epochs)	Perceiver + BioGPT		1280	224	proprietary
Prov-GigaPath	Microsoft / Providence	✅	May 2024*	DINOv2	170K	30K			LongNet	86M	1536	224	proprietary (Providence)
MADELEINE (VL)	Mahmood Lab	✅	Aug 2024*	contrastive (InfoNCE & OT)	16K	2K	120	90 epochs	multi-head attention MIL		512	256	ACROBAT, BWH Kidney (proprietary)
CHIEF (VL)	Yu Lab	✅	Sep 2024*
COBRA	Kather Lab	✅	Nov 2024*	COBRA (MoCo-v3 in FM embedding space)	3K	2.8K	1024	2K epochs	Mamba-2 + ABMIL	15M	768	224	TCGA (BRCA, CRC, LUAD, LUSC, STAD)
TITANV (VL)	Mahmood Lab	✅	Dec 2024*	iBOT	340K		1024	91K (270 epochs)	ViT (smaller)	42M		448	Mass-340K (proprietary)
THREADS (WSI, RNA, DNA)	Mahmood Lab	❌	Jan 2025*		47K		1200	up to 101 epochs	ViT-L			224	MBTG-47k (MGH, BWH, TCGA, GTEx)