/WSI_SOTA

The state of art performance in WSI based on deep learning

WSI_SOTA

The state-of-the-art performance on WSI using deep learning.Here we divide the wsi tasks into three parts:classification,segmentation and detection.

Classification

Dataset WSI number Dataset link Code link Paper Year Method Accuracy(slide) F1 AUC(slide) Precision Recall
TCGA-NSCLC 1053 https://portal.gdc.cancer.gov/repository https://github.com/cvlab-stonybrook/local learning wsi Gigapixel Whole-Slide Images Classification using Locally Supervised Learning (arxiv.org) 2022 Locally Supervised Learning 87.85 93.77
TCGA-RCC 939 https://portal.gdc.cancer.gov/repository https://github.com/cvlab-stonybrook/local learning wsi Gigapixel Whole-Slide Images Classification using Locally Supervised Learning (arxiv.org) 2022 Locally Supervised Learning 91.40 97.60
LKS 684 https://github.com/cradleai/LKS-Dataset https://github.com/cvlab-stonybrook/local learning wsi Gigapixel Whole-Slide Images Classification using Locally Supervised Learning (arxiv.org) 2022 Locally Supervised Learning 89.76
TCGA-GBM、TCGA-LGG 736 https://portal.gdc.cancer.gov/repository https://github.com/CityUAIM-Group/MultiModal-learning Discrepancy and Gradient-Guided Multi-modal Knowledge Distillation for Pathological Glioma Grading 2022 Multi-modal Knowledge Distillation 76.78 92.35
TCGA-LMS 85 https://github.com/machiraju-lab/UA-CNN Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification 0.83 ± 0.09 0.77 ± 0.10 0.75 ± 0.10 0.83 ± 0.0
CAMELYON16 400 https://camelyon17.grand-challenge.org/Data/ https://github.com/miccaiif/DGMIL DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification 2022 MIL 0.8018 0.8368
CAMELYON16 400 https://camelyon17.grand-challenge.org/Data/ https://github.com/TencentAILabHealthcare/ReMix ReMix：General and Efficient Framework for Multiple Instance Learning Based Whole Slide Image Classification 2022 MIL 0.9543 0.9639 0.9410
CAMELYON16 401 https://camelyon17.grand-challenge.org/Data/ https://github.com/PhilipChicco/FRMIL Feature Re-calibration Based Multiple Instance Learning for Whole Slide Image Classification 2022 MIL 0.8910 0.8950
TCGA Lung 1054 https://portal.gdc.cancer.gov/repository https://github.com/miccaiif/DGMIL DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification 2022 MIL 0.9200 0.9702
UniToPatho 292 https://ieee-dataport.org/open-access/unitopatho https://github.com/TencentAILabHealthcare/ReMix ReMix：General and Efficient Framework for Multiple Instance Learning Based Whole Slide Image Classification 2022 MIL 0.8068 0.7820 0.8094

Segmentation

Dataset WSI number Dataset link Code link Paper Year Method Accuracy F1 mIoU Precision Recall Dice AJI Hausdorff PQ
GlaS 16 https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/ https://github.com/xmed-lab/OEEM Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images (arxiv.org) 2022 CAM pseudo mask 87.35 77.56 87.36
Kumar (nucleus) 30 https://monuseg.grand-challenge.org/Data/ https://github.com/hust-linyi/insmix InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation 2022 Data Augmentation 82.65 63.31
DeepLIIF 1667 https://deepliif.org/ https://github.com/loic-lb/Unsupervised-Nuclei-Segmentation-using-Spatial-Organization-Priors Unsupervised Nuclei Segmentation Using Spatial Organization Priors 2022 Unsupervised GAN 79.65(Semantic) 87.89(Semantic) 66.30(Semantic) "69.81(Semantic)
63.47(Object)" 41.91(Object) 14.79(Object)
Warwick HER2 84 https://warwick.ac.uk/fac/cross_fac/tia/data/her2contest/ https://github.com/loic-lb/Unsupervised-Nuclei-Segmentation-using-Spatial-Organization-Priors Unsupervised Nuclei Segmentation Using Spatial Organization Priors 2022 Unsupervised GAN 81.64(Semantic) 75.71(Semantic) 64.34(Semantic) "58.46(Semantic)
58.65(Object)" 39.07(Object) 6.12(Object)
TNBC->KIRC/STAD 50(TNBC)/486(KIRC)/99(STAD) "https://zenodo.org/record/1175282#.Y2Ux2_dByiN
https://portal.gdc.cancer.gov/projects/TCGA-KIRC
https://www.cancerimagingarchive.net/" https://github.com/YashSharma/MaNi MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation 2022 Unsupervised Cross-Domain "0.733(KIRC)
0.776(TCIA)"
STAD->KIRC/TNBC 50(TNBC)/486(KIRC)/99(STAD) "https://zenodo.org/record/1175282#.Y2Ux2_dByiN
https://portal.gdc.cancer.gov/projects/TCGA-KIRC
https://www.cancerimagingarchive.net/" https://github.com/YashSharma/MaNi MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation 2022 Unsupervised Cross-Domain "0.727(KIRC)
0.821(TCIA)"
CoNSep->PanNuke 41(CoNSep)/481(PanNuke) "https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/
https://jgamper.github.io/PanNukeDataset/" https://github.com/YashSharma/MaNi MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation 2022 Unsupervised Cross-Domain 0.74 0.534 0.477
CAMELYON16 400 https://github.com/miccaiif/DGMIL DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification 2022 MIL
TCGA Lung 1054 https://portal.gdc.cancer.gov/repository https://github.com/miccaiif/DGMIL DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification 2022 MIL

Detection

Dataset WSI number Dataset link Code link Paper Year Method AUC
TCGA-CRC、TCGA-STAD 896+2675 https://portal.gdc.cancer.gov/projects/TCGA-STAD / Joint Region-Attention and Multi-scale Transformer for Microsatellite Instability Detection from Whole Slide Images in Gastrointestinal Cancer 2022 Multi-scale transformer 0.921

Contributors

Yuan Zhang，Hanhuan Cui，Jingjing Pei