Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning, CVPR 2022. [HTML] [arXiv] [Oral]
Richard. J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan*, Faisal Mahmood*

@inproceedings{chen2022scaling,
    author    = {Chen, Richard J. and Chen, Chengkuan and Li, Yicong and Chen, Tiffany Y. and Trister, Andrew D. and Krishnan, Rahul G. and Mahmood, Faisal},
    title     = {Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {16144-16155}
}

Key Ideas & Main Findings

Hierarchical Image Pyramid Transformer (HIPT) Architecture: Three-stage hierarchical ViT that formulates gigapixel whole-slide images (WSIs) as a disjoint set of nested sequences. HIPT unroll the WSI into non-overlapping [4096 × 4096] image regions, followed by unrolling each region into non-overlapping [256 × 256] image patches, and lastly each patch as non-overlapping [16 × 16] cell tokens. Our method is analgous to that of hierarchical attention networks in long document modeling, in which word embeddings within sentences are aggregated to form sentence-level embeddings and subsequently aggregated into document-level embeddings. Inference in HIPT is performed via bottom-up aggregation of [16 × 16] visual tokens in their respective [256 × 256] and [4096 × 4096] windows via Transformer attention to compute a slide-level representation.
Learning Context-Aware Token Dependencies in WSIs: Note that Transformer attention is computed only in local windows (instead of across the entire WSI), which makes learning long-range dependencies tractable. Though representation learning for [4096 × 4096] image regions may seem expensive, also note that the patch size at this level is [256 × 256], and thus has similar complexity of applying ViTs to [256 × 256] image patches with [16 × 16] tokens.
Hierarchical Pretraining: Since encoding [4096 x 4096] images is the same subproblem as encoding [256 x 256] images, we hypothesize that ViT pretraining techniques can generalize to higher resolutions with little modification. DINO is used to not only pretrain ViT-16 in HIPT, but also ViT-256 via [6 x 6] local and [14 x 14] global crops on a 2D grid-of-features (obtained by using VIT-16 as a patch tokenizer for ViT-256).
Self-Supervised Slide-Level Representation Learning: HIPT is evaluated via pretraining + freezing the ViT-16 / ViT-256 stages, with the ViT-4K stage finetuned with slide-level labels, assessed on cancer subtyping and survival prediction tasks in TCGA. We also perform self-supervised KNN evaluation of HIPT embeddings via computing the mean [CLS]-4K tokens extracted from ViT-256, as a proxy for the slide-level embedding. On Renal Cell Carcinoma subtyping, we report that averaged, pretrained HIPT-4K embeddings without any labels perform as well as CLAM-SB.

Updates / TODOs

Please follow this GitHub for more updates.

Removing dead code in HIPT_4K library.
Better documentation on interpretability code example.
Add pretrained models + instructions for hierarchical visualization.
Add pre-extracted slide-level embeddings, and code for K-NN evaluation.
Add weakly-supervised results for Tensorboard.

Pre-Reqs + Installation

This repository includes not only the code base for HIPT, but also saved HIPT checkpoints and pre-extracted HIPT slide embeddings with ~4.08 GiB of storage, which we version control via Git LFS.

To clone this repository without large files initially:

GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/mahmoodlab/HIPT.git 	# Pulls just the codebase
git lfs pull --include "*.pth"						# Pulls the pretrained checkpoints
git lfs pull --include "*.pt"						# Pulls pre-extracted slide embeddings
git lfs pull --include "*.pkl"						# Pulls pre-extracted patch embeddings
git lfs pull --include "*.png"						# Pulls demo images (required for 4K x 4K visualization)

To clone all files:

git clone https://github.com/mahmoodlab/HIPT.git

To install Python dependencies:

pip install -r requirements.txt

HIPT Walkthrough

How HIPT Works

Below is a snippet of a standalone two-stage HIPT model architecture that can load fully self-supervised weights for nested [16 x 16] and [256 x 256] token aggregation, defined in ./HIPT_4K/hipt_4k.py. Via a few einsum operations, you can put together multiple ViT encoders and have it scale to large resolutions. HIPT_4K was used for feature extraction of non-overlapping [4096 x 4096] image regions across the TCGA.

import torch
from einops import rearrange, repeat
from HIPT_4K.hipt_model_utils import get_vit256, get_vit4k

class HIPT_4K(torch.nn.Module):
    """
    HIPT Model (ViT_4K-256) for encoding non-square images (with [256 x 256] patch tokens), with 
    [256 x 256] patch tokens encoded via ViT_256-16 using [16 x 16] patch tokens.
    """
    def __init__(self, 
        model256_path: str = 'path/to/Checkpoints/vit256_small_dino.pth',
        model4k_path: str = 'path/to/Checkpoints/vit4k_xs_dino.pth', 
        device256=torch.device('cuda:0'), 
        device4k=torch.device('cuda:1')):

        super().__init__()
        self.model256 = get_vit256(pretrained_weights=model256_path).to(device256)
        self.model4k = get_vit4k(pretrained_weights=model4k_path).to(device4k)
        self.device256 = device256
        self.device4k = device4k
        self.patch_filter_params = patch_filter_params
	
    def forward(self, x):
        """
        Forward pass of HIPT (given an image tensor x), outputting the [CLS] token from ViT_4K.
        1. x is center-cropped such that the W / H is divisible by the patch token size in ViT_4K (e.g. - 256 x 256).
        2. x then gets unfolded into a "batch" of [256 x 256] images.
        3. A pretrained ViT_256-16 model extracts the CLS token from each [256 x 256] image in the batch.
        4. These batch-of-features are then reshaped into a 2D feature grid (of width "w_256" and height "h_256".)
        5. This feature grid is then used as the input to ViT_4K-256, outputting [CLS]_4K.

        Args:
          - x (torch.Tensor): [1 x C x W' x H'] image tensor.

        Return:
          - features_cls4k (torch.Tensor): [1 x 192] cls token (d_4k = 192 by default).
        """
        batch_256, w_256, h_256 = self.prepare_img_tensor(x)                    # 1. [1 x 3 x W x H].
        batch_256 = batch_256.unfold(2, 256, 256).unfold(3, 256, 256)           # 2. [1 x 3 x w_256 x h_256 x 256 x 256] 
        batch_256 = rearrange(batch_256, 'b c p1 p2 w h -> (b p1 p2) c w h')    # 2. [B x 3 x 256 x 256], where B = (1*w_256*h_256)


        features_cls256 = []
        for mini_bs in range(0, batch_256.shape[0], 256):                       # 3. B may be too large for ViT_256. We further take minibatches of 256.
            minibatch_256 = batch_256[mini_bs:mini_bs+256].to(self.device256, non_blocking=True)
            features_cls256.append(self.model256(minibatch_256).detach().cpu()) # 3. Extracting ViT_256 features from [256 x 3 x 256 x 256] image batches.

        features_cls256 = torch.vstack(features_cls256)                         # 3. [B x 384], where 384 == dim of ViT-256 [ClS] token.
        features_cls256 = features_cls256.reshape(w_256, h_256, 384).transpose(0,1).transpose(0,2).unsqueeze(dim=0) 
        features_cls256 = features_cls256.to(self.device4k, non_blocking=True)  # 4. [1 x 384 x w_256 x h_256]
        features_cls4k = self.model4k.forward(features_cls256)                  # 5. [1 x 192], where 192 == dim of ViT_4K [ClS] token.
        return features_cls4k

Using the HIPT_4K API

You can use the HIPT_4K model out-of-the-box, and use it to plug-and-play into any of your downstream tasks (example below).

from HIPT_4K.hipt_4k import HIPT_4K
from HIPT_4K.hipt_model_utils import eval_transforms

model = HIPT_4K()
model.eval()

region = Image.open('HIPT_4K/image_demo/image_4k.png')
x = eval_transforms()(region).unsqueeze(dim=0)
out = model.forward(x)

Hierarchical Interpretability

For hierarchical interpretability, please see the following notebook, which uses the following functions in ./HIPT_4K/hipt_heatmap_utils.py.

Downloading + Preprocessing + Organizing TCGA Data

Using the NIH Genomic Data Commons Data Portal and the cBioPortal, we downloaded diagnostic whole-slide images (WSIs) for 28 cancer types using the GDC Data Transfer Tool, followed by using the publicly-available CLAM library for tissue segmentation, tissue patching and feature extraction, which we modified for extracting both ResNet-50 features (pretrained on ImageNet) and ViT-16 features (pretrained on the TCGA). For patching at [256 × 256] resolution, we used default tissue segmentation parameters. For patching at [4096 × 4096] resolution, we additionally saved each [4096 × 4096] image region, which we used for ViT_256-16 and ViT_4096-256 pretraining (-16 suffix == using [16 × 16]-sized tokens in a ViT model, -256 suffix == using [256 × 256]-sized tokens in a ViT model). Extracted TCGA features are organized in the following directories:

Example Directory

TCGA_ROOT_DIR/
    └──tcga_acc/
        ├── ...
    └──tcga_blca/
        ├── ...
    └──tcga_brca/
        └── WSIs/
            ├── slide_1.svs
            ├── slide_2.svs
            └── ...
        └── extracted_mag20x_patch256_fp/
            └── masks/
                ├── slide_1.png
                ├── slide_2.png
                └── ...
            └── patches/
                ├── slide_1.h5
                ├── slide_2.h5
                └── ...
            └── stitches/
                ├── slide_1.png
                ├── slide_2.png
                └── ...
            └── resnet50_trunc_pt_patch_features/
                ├── slide_1.pt
                ├── slide_2.pt
                └── ...
            └── vits_tcga_pancancer_dino_pt_patch_features/
                ├── slide_1.pt
                ├── slide_2.pt
                └── ...
            └── process_list_autogen.csv
        └── extracted_mag20x_patch4096_fp/
            └── masks/
                ├── slide_1.png
                ├── slide_2.png
                └── ...
            └── patches/
                ├── slide_1.h5
                ├── slide_2.h5
                └── ...
            └── stitches/
                ├── slide_1.png
                ├── slide_2.png
                └── ...
            └── tar_patch_4096/
                ├── slide_1.tar
                ├── slide_2.tar
                └── ...
            └── vits_tcga_pancancer_dino_pt_patch_features/
                ├── slide_1.pt
                ├── slide_2.pt
                └── ...
            └── process_list_autogen.csv
    └──tcga_coadread/
        ├── ...
    ...
    └──tcga_ucec/
        ├── ...

Each cancer type is organized as its own folder in TCGA_ROOT_DIR, which additionally contains the following subfolders: In extracting patches at 20X magnification with non-overlapping patch sizes of 256, we create a results directory called extracted_mag20x_patch256_fp that will contain the following files / folders:

Folder Structure

WSIs/: Raw *.svs WSIs for that cancer type
extracted_mag20x_patch256_fp: Extracted features at 20× magnification for [256 × 256] patches (performed only for BRCA, COADREAD, LUAD, LUSC, CCRCC, CHRCC, PRCC, and STAD studies in TCGA). The _fp suffix represents the use of 'fast patching" as performed in CLAM, in which coordinates instead of raw patches are saved. This folder contains the following subfolders:
- masks/: Directory of segmented tissue-containing regions (one image per WSI).
- patches/: Directory of extracted image patches (one .h5 file per WSI, where each entry corresponds to the coordinates of the top-left corner of a patch)
- stitches/: Directory of downsampled visualizations of stitched tissue patches, used a sanity check to inspect whether we patched correctly (one image per WSI).
- resnet50_trunc_pt_patch_features/: Directory of pre-extracted ResNet-50 features (pretrained on ImageNet) for each patch within each WSI (with patches read via OpenSlide using coordinates in patches/, saved in a *.pt format. Each *.pt file is a [M × 1024]-sized Tensor containing extracted 1024-dim embeddings for M patches in the WSI.
- vits_tcga_pancancer_dino_pt_patch_features/: Directory of pre-extracted ViT-16 features (pretrained on TCGA) for each patch within each WSI (with patches read via OpenSlide using coordinates in patches/, saved in a *.pt format. Each *.pt file is a [M × 384]-sized Tensor containing extracted 384-dim embeddings for M patches in the WSI.
- process_list_autogen.csv: An auto-generated csv file that contains a list of all slides processed, along with their segmentation/patching parameters used.
extracted_mag20x_patch4096_fp: Extracted features at 20× magnification for [4096 × 4096] image regions, containing the following subfolders:
- masks/: Same as [256 × 256] setting.
- patches/: Same as [256 × 256] setting.
- stitches/: Same as [256 × 256] setting.
- tar_patch_4096/: Directory of saved [4096 × 4096] image regions for each WSI, stored in a *.tar format using WebDataset API.
- vits_tcga_pancancer_dino_pt_patch_features/: Directory of pre-extracted ViT-16 features (pretrained on TCGA) for each [4096 × 4096] region within each WSI (with regions read via OpenSlide using coordinates in patches/, saved in a *.pt format. Each *.pt file is a [M × 256 × 384]-sized Tensor containing extracted 384-dim embeddings for M regions in the WSI, which each region represented as as a 256-length sequence of [256 × 256] patch embeddings.
- process_list_autogen.csv: An auto-generated csv file that contains a list of all slides processed, along with their segmentation/patching parameters used. Note that in using a large image resolution for patching, not all WSIs are used in [4096 × 4096] evaluation.

Organizing the folders and subfolders for all of these different cancer types (with different features types too) allowed ease of running classification experiments.

Hierarchical Pretraining for ViT-16/256 Models + Pretrained Models

Example Directory

TCGA_PRETRAINING_DIR/
  └──patch_256_pretraining/
      ├── patch_1.png
      ├── patch_2.png
      └── ...
  └──region_4096_pretraining/
      ├── slide_1_1.pt
      ├── slide_1_2.pt
      └── ...
  └──ckpts/
      └── pretrain/
          └── vit256_s_dino.pth
          └── vit4k_xs_dino.pth

We set up the following directories for ViT_256-16 and ViT_4K-256 pretraining respectively:

.../path/to/patch_256_pretraining/: Directory of raw [256 × 256] patches (as *.png format) extracted from the tar_patch_4096/ subdirectories of each cancer type, used to pretrain ViT_256-16.
.../path/to/region_4096_pretraining/: Directory of pre-extracted ViT_4K-256 features for each [4096 × 4096] region across all WSIs (in total: 433779 regions). Each *.pt file is a [256 × 384]-sized Tensor, which is a 256-length sequence of pre-extracted ViT_256-16 features for each [256 × 256] patch. This folder is used to pretain ViT_4K-256.
./HIPT_4K/Checkpoints/: Directory for holding the pretrained weights, which we use for feature extraction. Our pretraining method largely follows the original DINO framework for conventional [256 × 256] image pretraining using ViT_256-16, which we extend to the [4096 × 4096] setting. Again, note that the -16 suffix refers to using [16 × 16]-sized tokens in a ViT model, and the -256 suffix using [256 × 256]-sized tokens in a ViT model. The following commands below are used for pretraining.

python -m torch.distributed.launch --nproc_per_node=8 main_dino.py --arch vit_small --data_path /path/to/TCGA_PRETRAINING_DIR/patch_256_pretraining/ --output_dir /path/to/TCGA_PRETRAINING_DIR/ckpts/pretrain/ --epochs 100
python -m torch.distributed.launch --nproc_per_node=8 main_dino4k.py --arch vit_xs --data_path /path/to/TCGA_PRETRAINING_DIR/region_4k_pretraining/ --output_dir /path/to/TCGA_PRETRAINING_DIR/ckpts/pretrain/ --epochs 100

SSL Strategy	ViT SSL	Dataset	Iteration	Batch Size	Arch	Image Size	Token Size	Dim	Download
Hierarchical Pretraining	DINO	TCGA	400,000	256	ViT-S/16	256	16	384	Backbone
Hierarchical Pretraining	DINO	TCGA	200,000	256	ViT-XS/256	4096	256	192	Backbone

Weakly-Supervised Training + Evaluation

Following ViT-16/256 pretraining and pre-extracting instance-level [256 × 256] features using ViT-16, we extend the publicly-available CLAM scaffold code for running 10-fold cross-validation experiments as well as implement several of the current weakly-supervised baselines. Our main method is hipt_lgp (abbreviated for HIPT with Local-Global Pretraining). We make available our saved results directory, evaluation code, and a Jupyter Notebook containing a walkthrough of our method.

Full List of Training Classification Commands

GPU=0
DATAROOT=/path/to/TCGA_ROOT_DIR/
TASK=tcga_brca_subtype
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25 --pretrain_4k vit4k_xs_dino
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0 --pretrain_4k vit4k_xs_dino
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25 --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0 --pretrain_4k vit4k_xs_dino --freeze_4k
TASK=tcga_kidney_subtype
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25 --pretrain_4k vit4k_xs_dino
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0 --pretrain_4k vit4k_xs_dino
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25 --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0 --pretrain_4k vit4k_xs_dino --freeze_4k
TASK=tcga_lung_subtype
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25 --pretrain_4k vit4k_xs_dino
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0 --pretrain_4k vit4k_xs_dino
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 0.25 --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --model_type hipt_lgp --task $TASK --prop 1.0 --pretrain_4k vit4k_xs_dino --freeze_4k

Analagously, we also use the MCAT scaffold code for survival prediction, and make available our saved results directory / tensorboard logs and evaluation code.

Full List of Training Survival Commands

DATAROOT=/path/to/TCGA_ROOT_DIR/
GPU=0
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --which_splits 5foldcv --split_dir tcga_brca --mode pyramid --model_type hipt_lgp --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --which_splits 5foldcv --split_dir tcga_coadread --mode pyramid --model_type hipt_lgp --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --which_splits 5foldcv --split_dir tcga_kirc --mode pyramid --model_type hipt_lgp --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --which_splits 5foldcv --split_dir tcga_kirp --mode pyramid --model_type hipt_lgp --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --which_splits 5foldcv --split_dir tcga_luad --mode pyramid --model_type hipt_lgp --pretrain_4k vit4k_xs_dino --freeze_4k
CUDA_VISIBLE_DEVICES=$GPU python main.py --data_root_dir $DATAROOT --which_splits 5foldcv --split_dir tcga_stad --mode pyramid --model_type hipt_n --pretrain_4k vit4k_xs_dino --freeze_4k

Understanding Baselines, Clarifications, and Future Work

In making the pretrained weights for HIPT fully-available, we hope that HIPT can be plugged-and-played in your experiments, and you would find the same level of improvement :). In building off of this work, we clarify a few details:

As slide-level tasks in the TCGA do not have official benchmarks, reported AUC performance may vary with different train-test splits. The results in this work use the following 10-fold CV and 5-fold CV train-test splits, which have been used consistently in prior works. Though the comparisons of MIL architecture performance are equivalent (all methods using same pretrained patch-level embeddings), general comparisons with MIL performance of prior works cannot be made, as: 1) different patch-level embeddings are used for training MIL methods (ImageNet ResNet-50 vs. SSL ViT-16), 2) a number of WSIs were excluded in each cohort, due to the lack of tissue content in patching at [4096 x 4096] resolution. To reproduce the results of this paper, you must use the exact train-test splits with the same pretrained embedding type.
Despite average ViT_4K-256 performing well in KNN evaluation, average ViT_256-16 embeddings did not perform as well as mean ResNet-50 (transferred from ImageNet) embeddings on some of the downstream tasks. Since Hierarchical Pretraining of ViT_4K-256 depends on pre-extracted ViT_256-16 embeddings, there is (of course) considerable room for improvement in boosting unsupervised and weakly-supervised slide-level performance in refining the ViT_256-16 encoder.

Issues

Please open new threads or report issues directly to [email protected].

Acknowledgements, License & Usage

We thank Felix Yu, Ming Y. Lu, Chunyuan Li, and the BioML group at Microsoft Research New England for their insightful feedback.
Code for Weakly-Supervised Subtyping + Survival Classification was largely adapted from CLAM and MCAT
Code for Hierarchical Pretraining was largely adapted via making modifications to DINO
Code for self-supervised evaluation was built on our previous NeurIPS workshop paper
If you found our work useful in your research, please consider citing our works(s) at:

@article{chen2022self,
    author    = {Chen, Richard J and Krishnan, Rahul G},
    title     = {Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology},
    journal   = {Learning Meaningful Representations of Life, NeurIPS 2021},
    year      = {2021},
}

@inproceedings{chen2022scaling,
    author    = {Chen, Richard J. and Chen, Chengkuan and Li, Yicong and Chen, Tiffany Y. and Trister, Andrew D. and Krishnan, Rahul G. and Mahmood, Faisal},
    title     = {Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {16144-16155}
}

Any work that cites HIPT should also cite the original Vision Transformer and DINO.

mahmoodlab/HIPT

mahmoodlab

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